Sensing systems and methods for diagnosing, staging, treating, and assessing risk of liver disease using monitored analyte data - Patent Application 20070233633

Abstract

Certain aspects of the present disclosure relate to methods and systems for generating and utilizing analyte measurements. In certain aspects, a monitoring system includes 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 priority to and the benefit of U.S. Provisional Patent Application No. 63 / 267,447, filed February 2, 2022, U.S. Provisional Patent Application No. 63 / 403,568, filed September 2, 2022, and U.S. Provisional Patent Application No. 63 / 403,582, filed September 2, 2022, which are assigned to the assignee of this application and are expressly incorporated by reference in their entireties herein as if fully set forth below and for all applicable purposes. [Background technology]

[0002] Liver disease, also called hepatic disease, is any disorder of liver function that causes illness. The liver is responsible for many important functions in the human body, from protein production and blood clotting to cholesterol, lactate, glucose, and iron metabolism. When the liver is diseased or damaged, the impairment or loss of these functions can cause significant damage to the human body.

[0003] Liver diseases are generally classified as either acute or chronic based on the duration of the disease. Liver diseases can be caused by infection, injury, exposure to drugs or toxic compounds, alcohol, impurities in food, abnormal accumulation of normal substances in the bloodstream, autoimmune processes, genetic defects (such as hemochromatosis), and / or unknown causes. Common liver diseases include cirrhosis, liver fibrosis, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), hepatic ischemia-reperfusion injury, primary biliary cirrhosis (PBC), and hepatitis.

[0004] The American Liver Foundation estimates that over 20% of the population has NAFLD. It has been suggested that obesity, unhealthy diet, and sedentary lifestyles may contribute to the high prevalence of NAFLD. When left untreated, NAFLD can progress to NASH, which can cause serious adverse effects on the body. Once NASH develops, a person may experience liver swelling and scarring (i.e., cirrhosis) over time.

[0005] Lactate is measured and analyzed using a variety of approaches, including central laboratory methods, near-patient blood gas analysis, and analysis using portable point of care (POC) handheld devices. Central laboratory methods involve transporting a patient's blood sample to a laboratory via a porter or air-tube system. Unfortunately, central laboratory methods often result in a long time between when blood is drawn and when the clinician is made aware of the test result, which can result in delayed clinical decision making.

[0006] As POC technology advances, benchtop blood gas analyzers have become available for lactate testing near the patient. However, these devices are not portable and their availability is usually limited to individual specialty departments, such as emergency departments (EDs) and intensive care units (ICUs). Furthermore, sample turnaround time for test results can be affected by delays in transport to the ED or ICU when samples are drawn outside of these main units.

[0007] For this reason, small handheld devices, much like glucose meters, have become available for lactate measurement and analysis. Users may carry self-monitoring lactate monitors, which typically require the user to prick their finger to measure their lactate levels. However, given the inconvenience associated with the traditional finger-prick method, it is unlikely that users will perform lactate measurements in a timely manner. As a result, users not engaging with the device can have devastating consequences. In particular, users who forget to engage with the device may also be unable to manage their condition other than using the device. If the condition is left unmanaged for too long, the user's liver condition may deteriorate significantly, additional health challenges may arise, and in some cases, may lead to an increased risk or possibility of death. Summary of the Invention [Means for solving the problem]

[0008] One aspect of the present disclosure provides 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. [Brief description of the drawings]

[0009] So that the above-listed features of the present disclosure can be understood in detail, a more particular description, briefly summarized above, can be made by reference to embodiments, some of which are illustrated in the drawings. It should be noted, however, that the attached drawings merely illustrate certain exemplary embodiments of the present disclosure and therefore should not be considered as limiting its scope, since the description may be allowed for other equally valid embodiments.

[0010] [Figure 1]1 illustrates aspects of an exemplary decision support system that may be used in connection with implementing embodiments of the present disclosure. [Diagram 2] FIG. 1 conceptually illustrates an exemplary continuous analyte monitoring system including an exemplary continuous analyte sensor having sensor electronics, in accordance with certain aspects of the present disclosure. [Diagram 3] 2 illustrates example inputs and example metrics calculated based on the inputs for use by the decision support system of FIG. 1 according to certain embodiments disclosed herein. [Figure 4] FIG. 1 is a flow diagram illustrating an exemplary method for providing decision support using a continuous analyte sensor including at least a continuous lactate sensor, according to some exemplary aspects of the present disclosure. [Diagram 5] 1 is an exemplary workflow for determining hepatic lactate clearance rate using at least a continuous lactate monitor, according to certain embodiments of the present disclosure. [Figure 6] FIG. 1 is a flow diagram depicting a method for training a machine learning model to provide a prediction of liver disease diagnosis, according to certain embodiments of the present disclosure. [Figure 7] FIG. 1 is a block diagram illustrating an example computing device configured to execute a decision support engine in accordance with certain embodiments of the present disclosure. [Figure 8A] 1 depicts an exemplary enzyme domain configuration for a continuous multi-analyte sensor according to certain embodiments of the present disclosure. [Figure 8B] 1 depicts an exemplary enzyme domain configuration for a continuous multi-analyte sensor according to certain embodiments of the present disclosure. [Figure 8C] 1 depicts an exemplary enzyme domain configuration for a continuous multi-analyte sensor according to certain embodiments of the present disclosure. [Figure 8D] 1 depicts an exemplary enzyme domain configuration for a continuous multi-analyte sensor according to certain embodiments of the present disclosure. [Figure 8E] 1 depicts an exemplary enzyme domain configuration for a continuous multi-analyte sensor according to certain embodiments of the present disclosure. [Figure 9A] 1 depicts an alternative diagram of an exemplary dual electrode enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure. [Figure 9B] 1 depicts an alternative diagram of an exemplary dual electrode enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure. [Figure 9C] 1 depicts an alternative diagram of an exemplary dual electrode enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure. [Figure 9D] 1 depicts an alternative diagram of an exemplary dual electrode enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure. [Figure 9E] 1 depicts an exemplary dual electrode configuration for a continuous multi-analyte sensor according to certain embodiments of the present disclosure. [Figure 10A] 1 depicts an exemplary enzyme domain configuration for a continuous multi-analyte sensor according to certain embodiments of the present disclosure. [Figure 10B] 1 depicts alternative exemplary enzyme domain configurations for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure. [Figure 10C] 1 depicts alternative exemplary enzyme domain configurations for a continuous multi-analyte sensor, according to certain embodiments of the present disclosure. [Figure 11] 1 depicts an exemplary enzyme domain configuration for a continuous multi-analyte sensor according to certain embodiments of the present disclosure. [Figure 12A] 1 depicts alternative diagrams of exemplary dual electrode enzyme domain configurations G1-G4 for continuous multi-analyte sensors according to certain embodiments of the present disclosure. [Figure 12B] 1 depicts alternative diagrams of exemplary dual electrode enzyme domain configurations G1-G4 for continuous multi-analyte sensors according to certain embodiments of the present disclosure. [Figure 12C] 1 depicts alternative diagrams of exemplary dual electrode enzyme domain configurations G1-G4 for continuous multi-analyte sensors according to certain embodiments of the present disclosure. [Figure 12D]1 depicts alternative diagrams of exemplary dual electrode enzyme domain configurations G1-G4 for continuous multi-analyte sensors according to certain embodiments of the present disclosure. [Figure 13A] 1 depicts an exemplary lactate sensor according to certain embodiments of the present disclosure. [Figure 13B] 13B depicts a cross-sectional view of an electroactive section of the exemplary lactate sensor of FIG. 13A in accordance with certain embodiments of the present disclosure. [Figure 14A] 1 depicts an exemplary embodiment of a continuous analyte sensor system implemented as a wearable lactate sensor, according to certain embodiments of the present disclosure. [Figure 14B] 1 depicts an exemplary embodiment of a continuous analyte sensor system implemented as a wearable lactate sensor, according to certain embodiments of the present disclosure. [Figure 14C] 1 depicts an exemplary embodiment of a continuous analyte sensor system implemented as a wearable lactate sensor, according to certain embodiments of the present disclosure.

[0011] For ease of understanding, wherever possible, the same reference numerals have been used to designate identical elements that are common to each of the figures. It is contemplated that elements disclosed in one embodiment may be beneficially utilized in other embodiments without specific recitation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Liver disease is not easy to detect. In particular, the liver is sometimes called the silent organ, because even if liver failure occurs, the symptoms often go unnoticed. In some cases, liver disease may already reach an advanced stage when symptoms such as jaundice become evident. Therefore, early liver disease diagnosis and staging are essential to effectively treat and, in some cases, reverse the disease.

[0013] Disease diagnosis is the act or process of identifying or determining the nature and cause of disease, while disease staging is a clinical severity-based measure that uses objective medical criteria to assess the stage of progression of identified disease.More specifically, disease staging can provide important information about the extent of liver disease in a patient's body and the patient's expected response to different forms of treatment.

[0014] Physicians use information from a patient's medical history, physical examination, laboratory findings, and other diagnostic tests to diagnose and stage disease and prescribe appropriate treatment. For example, liver function tests can be used by physicians to screen for liver infections, monitor the progression of liver disease, assess the effectiveness of various treatments for liver disease, and monitor possible side effects of medications on a patient's liver, to name a few. Liver function tests check the levels of certain enzymes and proteins in a patient's blood. Higher or lower than normal levels may indicate liver damage in some cases.

[0015] However, such conventional liver disease diagnosis and staging methods face many challenges with regard to efficiency, accuracy, and delay in providing a liver diagnosis for treatment decision-making. For example, it can be very difficult to determine which particular diagnosis is indicated by a particular combination of symptoms, especially when the symptoms, such as fatigue, are non-specific. Liver disease can also present atypically with a constellation of abnormal and unexpected symptoms. Currently, the standard of care for a definitive diagnosis of liver disease is a liver biopsy. A liver biopsy is a non-scalable and invasive method for diagnosing liver disease. Thus, making an accurate diagnosis can prove to be particularly challenging for physicians. Furthermore, when a patient seeks health care, there is an iterative process of information gathering, information integration and interpretation, and determining a working diagnosis, and throughout the diagnostic process, there is a continuing assessment of whether sufficient information has been gathered. If the physician is not satisfied that the necessary information has been gathered to explain the patient's health problem or to accurately diagnose the patient with liver disease, or that the available information is consistent with a liver disease diagnosis, the process of information gathering, information integration and interpretation, and development of a working diagnosis continues. Thus, diagnosis of liver disease may be delayed and, in some cases, given the time-dependent nature of many diseases, including liver disease, may contribute to patients experiencing worsening symptoms, decline in overall health, and even death.

[0016] Furthermore, existing technologies such as point-of-care (POC) devices have been introduced to allow timely assessment of patients with or at risk for liver disease. As mentioned above, one such POC device may include a portable self-monitoring lactate monitor, which typically requires the user to prick their finger to obtain a single standalone reading indicative of their lactate level to diagnose liver health. So far, POC devices have almost exclusively included diagnostic devices, i.e., devices that can analyze a patient to obtain a single standalone reading. Thus, existing devices suffer from the technical problem of being unable to continuously (and / or semi-continuously and / or periodically) monitor the concentration of a changing analyte, such as lactate, to obtain a continuous (and / or semi-continuously and / or periodic) readout. Such continuous monitoring of an analyte is advantageous in diagnosing and staging a patient's disease, considering that continuous measurements provide continuously up-to-date measurements as well as information regarding the trend and rate of analyte change over continuous periods of time.

[0017] The continuous measurements proposed herein provide a more accurate indicator of liver metabolic function and liver health compared to a single time point reading. A single time point reading may be affected by patient activity such as exercise or dietary changes near or during that time point. Additionally, imaging techniques that determine structural aspects of the liver do not provide information about the metabolic functional performance of liver cell tissue. Measuring analytes (e.g., lactate) with the continuous readouts proposed herein may increase understanding of liver metabolic function to confirm good liver performance or to determine the presence and / or magnitude of liver metabolic dysfunction. Such information may also be used to make more informed decisions in assessing liver health and treating liver disease. As a result of this technical problem, diagnosing liver disease or its risk may be inaccurate and in some cases may prove life-threatening for patients with liver disease.

[0018] Thus, certain embodiments described herein provide a technical solution to the technical problem described above by providing decision support regarding liver disease using a continuous analyte monitoring system including at least a continuous lactate sensor. As used herein, the term "continuous" may mean fully continuous, semi-continuous, periodic, etc. The decision support may be provided in the form of a risk assessment, diagnosis, staging, and / or recommendation for treatment of liver disease, as described in more detail herein. As used herein, risk assessment may refer to an evaluation or estimation of a patient's liver disease reaching a more advanced stage, risk of death, risk of liver cancer, etc.

[0019] In certain embodiments, the continuous analyte monitoring system may provide decision support to the patient based on various collected data including analyte data, patient information, secondary sensor data (e.g., non-analyte data), etc. For example, the analyte data may include continuously monitored lactate data in addition to other continuously monitored analyte data such as glucose, ketones, and potassium.

[0020] The continuously monitored lactate data may be indicative of or may be used to determine a patient's lactate level, lactate production rate, lactate metabolism, and / or lactate clearance rate. Certain embodiments of the present disclosure provide techniques and systems for more accurately determining a patient's lactate clearance rate using continuously monitored lactate data, as described further below, and for correcting the patient's lactate clearance rate by using measurements associated with non-analyte sensor data and / or other patient information. As described above, the collected data also includes patient information, which may include information related to age, sex, family history of liver disease, other health conditions, etc. Secondary sensor data may include accelerometer data, heart rate data, body temperature, blood pressure, or any other sensor data other than analyte data.

[0021] According to an embodiment of the present disclosure, the decision support system presented herein is designed to provide disease decision support to assist in diagnosing patients who have or are at risk for liver disease, and in managing their liver disease or risk thereof. Providing liver disease decision support may involve using a large amount of collected data, including, for example, the analyte data, patient information, and secondary sensor data described above, to (1) automatically detect and classify abnormal liver conditions, (2) assess the presence and severity of liver disease, (3) risk stratify patients to identify patients at high risk for liver disease, (4) identify risks (e.g., mortality risk, liver cancer risk, etc.) associated with a current liver disease diagnosis, (5) make patient-specific treatment decisions or recommendations for liver disease, and (6) provide information regarding the effectiveness of an intervention (e.g., the effectiveness of a patient's lifestyle changes, the effectiveness of a surgical procedure, the effectiveness of a patient taking a new medication, etc.). In other words, the decision support system presented herein may provide information that helps direct and improve care for patients who have or are at risk for liver disease.

[0022] In certain embodiments, the decision support systems described herein may use various algorithms or artificial intelligence (AI) models, such as machine learning models, trained on patient-specific and / or population data to provide real-time decision support to patients based on collected information about the patient. For example, certain aspects are directed to algorithms and / or machine learning models designed to assess the presence and severity of liver disease in a patient. The algorithms and / or machine learning models may be used in combination with one or more continuous analyte sensors, including at least a continuous lactate sensor, to provide assessment and staging of liver disease, for example, at regular intervals (e.g., daily, weekly, etc.). In particular, the algorithms and / or machine learning models may take into account parameters such as the patient's lactate clearance rate (including lactate half-life), lactate levels, lactate rate of change, fasting lactate, postprandial lactate, lactate production rate, and lactate baseline when diagnosing and staging liver disease.

[0023] Based on these parameters, the algorithm and / or machine learning model can provide a risk assessment of one or more liver disease types and severity, and the patient's progression to one or more of these liver disease types. The algorithm and / or machine learning model can take into account population data, personalized patient-specific data, or a combination of both, when diagnosing and staging liver disease for a patient.

[0024] According to certain embodiments, prior to deployment, the machine learning model is trained with training data, including, for example, population data. As described in more detail herein, the population data may be provided in the form of a dataset including data records of historical patients with various stages of liver disease. Each data record is then characterized (e.g., refined into a set of one or more features or predictive variables) and labeled. Data labeling is the process of adding one or more meaningful and informative labels to provide context to the data for learning by the machine learning model. In certain embodiments, each data record is labeled with its corresponding liver disease diagnosis, assigned disease score, risk assessment, etc. The features associated with each data record may be used as input to the machine learning model, and the generated output may be compared to the label assigned to each of the data records. The model may calculate a loss based on the difference between the generated output and the provided label. This loss may then be used to modify the internal parameters or weights of the model. By iteratively processing the features associated with each data record corresponding to each historical patient, the model may be iteratively refined to generate accurate predictions of the presence and severity of liver disease in the patient.

[0025] The combination of the continuous analyte monitoring system with machine learning models and / or algorithms for diagnosing, staging, and assessing the risk of liver disease provided by the decision support system described herein allows for real-time diagnosis and allows for early intervention. In particular, the decision support system can be used to provide early alerts of liver decompensation and / or deliver information about other complications associated with the liver. Early detection of such decompensation and / or other complications can allow for intervention at the earliest possible stage to ultimately improve liver disease outcomes. For example, early intervention can reduce hospitalizations, complications, and deaths in some cases. In addition, the baseline lactate levels and changes in lactate levels provided by the continuous analyte monitoring system can be used as inputs to machine learning models and / or algorithms to triage patients for more urgent care. In patients at risk of sepsis or septic shock, traumatic brain injury, acute kidney injury, hepatic encephalopathy, or end-stage liver disease, increases in lactate can be used to inform urgent medical intervention.

[0026] In addition, through the combination of a continuous analyte monitoring system with machine learning and / or algorithms for diagnosing, staging, and assessing the risk of liver disease, the decision support system described herein may provide the necessary accuracy and reliability that patients expect. For example, bias, human error, and emotional impact may be minimized when assessing the presence and severity of liver disease in a patient. Furthermore, machine learning models and algorithms combined with an analyte monitoring system may provide insight into patterns and / or trends that may have previously been overlooked that may be detrimental to a patient's health, at least with respect to the liver. Thus, the decision support system described herein may aid in the identification of liver health for diagnostic, preventative, and therapeutic purposes.

[0027] Exemplary decision support system including an exemplary analyte sensor for diagnosing, staging, treating, and assessing risk of liver disease - Patents.com 1 illustrates an exemplary decision support system (also referred to as a "monitoring system") 100 for diagnosing, staging, treating, and assessing risk of liver disease in a user 102 (individually referred to herein as a user and collectively referred to herein as a user) using a continuous analyte monitoring system 104 including at least a continuous lactate sensor. The user may be a patient, in certain embodiments, or in some cases, a caregiver for the patient. In certain embodiments, the system 100 includes the continuous analyte monitoring system 104, a display device 107 running an application 106, 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.

[0028] 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 component within a biological fluid (e.g., blood, interstitial fluid, cerebrospinal fluid, lymphatic fluid, or urine) that can be analyzed. Analytes can include naturally occurring substances, man-made substances, metabolites, and / or reaction products. Analytes measured by the present devices and methods may include, but are not limited to, potassium, glucose, acarboxyprothrombin; acylcarnitines; adenine phosphoribosyltransferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profile (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-peptide; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-beta hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclo sporin A; d-penicillamine; deethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylation polymorphism, alcohol dehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne / Becker muscular dystrophy, glucose-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, D-Punjab, beta-thalassemia, hepatitis B virus , HCMV, HIV-1, HTLV-1, Leber's hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sex differentiation, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diphtheria / tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids / acylglycines; free beta-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4);Free 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-α-hydroxyprogesterone; hypoxanthine phosphoribosyltransferase; immunoreactive trypsin; lactate; lead; lipoproteins ((a), B / A-1, β); lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin; phytanic acid / pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse triiodothyronine (reverse tri-iodothyronine, rT3); selenium; serum pancreatic lipase; sisomicin; somatomedin C; specific antibodies (adenovirus, antinuclear antibody, anti-zeta antibody, arbovirus, pseudorabies virus, dengue virus, guinea worm, tapeworm, ameba histolytica, enterovirus, giardiasis, Helicobacter pylori, hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani, Leptospira, measles / mumps / rubella, Mycobacterium leprae, Mycoplasma pneumoniae, myoglobin, Onchocerca volvulus, parainfluenza Viruses, malaria parasites, poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, Rickettsia (tsutsugamushi disease), Schistosoma mansoni, Toxoplasma gondii, Treponema pallidum, Trypanosoma cruzi / langeri, Vesicular stomatitis virus, Wuchereria bancrofti, Yellow fever virus);Specific antigens (Hepatitis B virus, HIV-1);Succinylacetone;Sulfadoxine;Theophylline;Thyrotropin (TSH);Thyroxine (T4);Thyroxine-binding globulin;Trace elements;Transferrin;UDP-galactose-4-epimerase;Urea;Uroporphyrinogen I synthase;Vitamin A;Leukocytes;and zinc protoporphyrin. Salts, sugars, proteins, fats, vitamins, and hormones naturally occurring in blood or interstitial fluids can also constitute analytes in certain implementations. Analytes can be naturally occurring in biological fluids, such as metabolites, hormones, antigens, antibodies, and the like. Alternatively, analytes can be introduced into the body or can be exogenous, such as contrast agents for imaging, radioisotopes, chemical agents, fluorocarbon-based synthetic blood, challenge agent analytes (e.g., introduced for the purpose of measuring the increase and / or decrease in the rate of change of the concentration of the challenge agent analyte or other analytes 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, Silurt, Preluzine, Didrex, Prestate, Boranil, Sandrex, Pregin;Antidepressants (barbiturates, tranquilizers such as methaqualone, Valium, Librium, Miltown, Serax, Equuanil, Tranxine);Hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin);Nucleic acids (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tasionex, fentanyl, Darvon, Talwin, Lomotil);Synthetic narcotics (fentanyl, meperidine, amphetamine, methamphetamine, and phencyclidine analogues, e.g., ecstasy);Anabolic steroids;Analytes that can be analyzed include, but are not limited to, 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, and other chemicals produced in the body.

[0029] Analytes measured and analyzed by the devices and methods described herein include lactate, and optionally glucose, ketones, and / or potassium, although other analytes, including but not limited to those listed above, may also be considered.

[0030] 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 and / or an interface engine (not shown in FIG. 1 ). An EMR system is a software platform that allows for electronic entry, storage, and maintenance of digital medical data. An interface engine is a data synchronization tool to ensure that the EMR database and other system databases are synchronized over a network. EMR systems are commonly used throughout hospitals and / or other care facilities to document clinical information about patients over time. EMR systems organize and present the data in a manner that aids clinicians, for example, by interpreting health conditions and providing ongoing care, scheduling, billing, and follow-up. Data contained in an EMR system can also be used to generate reports for the patient's clinical care and / or disease management.

[0031] In certain embodiments, the continuous analyte monitoring system 104 is configured to continuously measure one or more analytes and transmit the analyte measurements to the display device 107 for use by the application 106. In some embodiments, the continuous analyte monitoring system 104 transmits the analyte measurements to the display device 107 through a wireless connection (e.g., a Bluetooth connection, a WiFi connection, and / or NFC). The transmission of the analyte measurements may be continuous (e.g., fully continuous, semi-continuous, or periodic), broadcast, or on-demand. 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. The continuous analyte monitoring system 104 may be described in more detail with respect to FIG. 2.

[0032] Application 106 is a mobile health application configured to receive and analyze analyte measurements from 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.

[0033] 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 certain 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 or the analyte sensor system 104 (e.g., the sensor electronics module 204 of FIG. 2). As discussed in more detail herein, the decision support engine 114 may provide decision support recommendations to the user via the application 106. The decision support engine 114 provides decision support recommendations based on information contained in the user profile 118.

[0034] The user profile 118 may include information collected about the user from the application 106. For example, the application 106 provides a set of inputs 128 including analyte measurements received from the continuous analyte monitoring system 104 that 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 through manual user input, one or more other non-analyte sensors or devices, other applications running on the display device 107, etc. The non-analyte sensors and devices include, but are not limited to, one or more of an insulin pump, a respiration sensor, a sensor or device provided by the display device 107 (e.g., an accelerometer, 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. The inputs 128 of the user profile 118 provided by the application 106 are described in further detail below with respect to FIG.

[0035] The DAM 113 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 condition, trends associated with the user's health or condition, etc. In certain 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.

[0036] The user profile 118 also includes demographic information 120, disease progression information 122, and / or medical information 124. In certain embodiments, such information may be provided via user input or may be obtained from a particular data store (e.g., 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 progression information 122 may include information regarding the user's disease, such as whether the user was previously diagnosed with cirrhosis, liver fibrosis, NAFLD, NASH, hepatic ischemia-reperfusion injury, primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), or whether the user was previously diagnosed with a liver disease caused by a virus, such as hepatitis A, hepatitis B, or hepatitis C. In certain embodiments, information regarding the user's disease may also include length of time since diagnosis, level of disease control, level of compliance with liver disease management therapy, predicted liver function, and / or other types of diagnoses (e.g., heart disease, obesity) or health measures (e.g., heart rate, exercise, stress, sleep, etc.), and / or the like.

[0037] In certain embodiments, medication information 124 may include information regarding the amount, frequency, and type of medication taken by the user.

[0038] In certain embodiments, the medication information may include information regarding the consumption of one or more drugs known to damage the liver (e.g., affect lactate clearance) and / or cause liver toxicity. The one or more drugs known to damage the liver and / or cause liver toxicity may include antibiotics such as amoxicillin / clavulanate, clindamycin, erythromycin, nitrofurantoin, rifampin, sulfonamides, tetracyclines, trimethoprim / sulfamethoxazole, and drugs used to treat tuberculosis (isoniazid, pyrazinamide), anticonvulsants such as talbamazepine, tetrabarbital, phenytoin, and valproate, bupropion, fluoxetine, mirtazapine, paroxetine, sertraline, trazodone, and amitriptyline. antidepressants such as tricyclic antidepressants such as ketoconazole and terbinafine; antihypertensives (e.g., medicines used to treat high blood pressure or kidney or heart disease) such as captopril, enalapril, irbesartan, lisinopril, losartan, and verapamil; antipsychotics such as phenothiazines (e.g., chlorpromazine) and risperidone; heart medications such as amiodarone and clopidogrel; hormone modifiers such as anabolic steroids, birth control pills (oral contraceptives), and estrogens; anti-inflammatory drugs, such as NSAIDs), acarbose (e.g., a drug for treating diabetes), allopurinol (e.g., a drug for treating gout), antiretroviral therapy (ART) drugs (e.g., a drug for treating human immunodeficiency virus (HIV) infection), baclofen (e.g., a muscle relaxant), cyproheptadine (an antihistamine, such as an antihistamine), azathioprine (used, for example, to prevent rejection of organ transplants), methotrexate (e.g., used to treat cancer), omeprazole (e.g., used to treat gastroesophageal reflux), PD-1 / PD-L1 inhibitors (e.g., an anticancer drug), statins (e.g., used to treat high cholesterol levels), and many types of chemotherapy including immune checkpoint inhibitors.

[0039] In certain embodiments, the dosing information may include information regarding the intake of one or more drugs known to improve liver function. The one or more drugs known to improve liver function may include ademetionine, abatrombopag, dehydroemetine, entecavir, glecaprevir and pibrentasvir, lamivudine, metadoxine, methionine, sofosbuvir, velpatasvir, and voxilaprevir, telbivudine, tenofovir, trientine, ursodeoxycholic acid, and the like.

[0040] In certain embodiments, the user profile 118 is dynamic because at least a portion of the information stored in the user profile 118 may be 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.

[0041] 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, a file system including a hierarchical file system, etc. In some example implementations, user database 110 is distributed. For example, user database 110 may comprise multiple persistent storage devices that are distributed. Additionally, user database 110 may be replicated such that the storage devices are geographically distributed.

[0042] The user database 110 includes user profiles 118 associated with multiple users who similarly interact with the applications 106 executing on the display devices 107 of other users. The user profiles stored in the user database 110 are accessible to the applications 106 as well as the decision support engine 114. The user profiles in the user database 110 may be accessible to the applications 106 and the decision support engine 114 via one or more networks (not shown). As described above, the decision support engine 114, and more specifically the DAM 116 of the decision support engine 114, can fetch inputs 128 from the user database 110 and then calculate multiple metrics 130, which may be stored as application data 126 in the user profiles 118.

[0043] In certain embodiments, the user profiles 118 stored in the user database 110 may also be stored in the medical history record database 112. The user profiles 118 stored in the medical history record database 112 may provide a repository of up-to-date information and medical history information for each user of the application 106. Thus, the medical history record database 112 essentially provides all data related to each user of the application 106, 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, for example, identify when information related to a user was obtained and / or updated.

[0044] Additionally, the data stored in the medical history database 112 may maintain time series data collected about a user over the period of time the user uses the continuous analyte monitoring system 104 and application 106. For example, analyte data for a user who used the continuous analyte monitoring system 104 and application 106 for five years to manage the user's liver condition may have the time series analyte data associated with the user maintained for the five years.

[0045] Additionally, in certain embodiments, historical record database 112 may include data for one or more patients who are not users of continuous analyte monitoring system 104 and / or application 106. For example, historical record database 112 may include information (e.g., user profiles) about one or more patients who have not been previously diagnosed with liver disease, as analyzed, for example, by a health care physician (or other known method), as well as information (e.g., user profiles) about one or more patients who have been previously diagnosed with liver disease, as analyzed, for example, by a health care physician (or other known method). The data stored in historical record database 112 may be referred to herein as population data.

[0046] 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 associated with the patient over the lifespan of the disease, including information about the patient before he was diagnosed with liver disease, and information related to each stage of liver disease that has progressed and / or regressed in the patient, as well as information related to other diseases, such as renal disease or similar diseases coexisting in association with liver disease. Such information may indicate the patient's symptoms, the patient's physiological state, the patient's lactate level, the patient's glucose level, the patient's ketone level, the patient's potassium level, the status / condition of one or more organs of the patient, the patient's habits (e.g., alcohol consumption, activity level, food consumption, etc.), prescribed medications, etc., throughout the lifespan of the disease.

[0047] 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. That is, 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 storage server running on a public or private cloud.

[0048] As mentioned above, the decision support system 100 is configured to diagnose, stage, treat, and assess a user's risk of liver disease using a continuous analyte monitoring system 104 including at least a continuous lactate sensor. In certain embodiments, to enable such diagnosis and staging, the decision support engine 114 is configured to provide real-time and / or non-real-time liver disease decision support to the user and / or others, including, but not limited to, health care providers, the user's family, the user's caregivers, researchers, artificial intelligence (AI) engines, and / or other individuals, systems, and / or groups that assist with 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 stored in the user database 110 and perform analysis on that information for determining a probability of the presence and / or severity of liver disease for the user and for providing one or more recommendations for treatment based at least in part on the determination. The user profile 118 may be accessible to the decision support engine 114 via one or more networks (not shown) to perform such analysis.

[0049] In certain embodiments, the decision support engine 114 may utilize one or more trained machine learning models that enable the decision support engine 114 to perform analysis on information collected / received from 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. Although 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, or 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 many virtual machines (VMs) that run at least in part on one or many physical servers in relational and / or non-relational database formats.

[0050] 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) related to 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 diagnosed with various stages of liver disease, as well as patients (e.g., healthy patients) who have not previously been diagnosed with liver disease. 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.

[0051] Training data refers to a characterized and labeled data set. For example, the data set may include a plurality of data records, each containing information corresponding to a different user profile stored in the user database 110, and each data record is characterized and labeled. In machine learning and pattern recognition, a feature is an individual measurable characteristic or feature. Generally, the feature that best characterizes the pattern in the data is selected to create a predictive machine learning model. Data labeling is the process of adding one or more meaningful and informative labels to provide context to the data for learning by the machine learning model. As an illustrative example, each relevant feature of a user reflected in the corresponding data record may be a feature used in training the machine learning model. Such features may include age, sex, the average change (e.g., average delta) in lactate clearance from a first timestamp to a second timestamp, the average change (e.g., average delta) in liver disease diagnosis from a first timestamp to a subsequent timestamp, the derivative of a measured linear system of lactate measurements at a point at a particular timestamp, and / or the difference in derivatives to determine the rate of change of the slope of the increase or decrease in value, etc. Additionally, the data records are labeled with indicators such as liver disease diagnoses, assigned disease scores, and / or identified risk of liver disease associated with the patient in the user profile.

[0052] 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 the 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, the model may be iteratively refined to generate accurate predictions of liver disease risk, presence, progression, improvement, and severity in the patient.

[0053] 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 and stored in the user database 110, use information in the user profile 118 as input to the trained models, and output a prediction indicating the presence and / or severity of the user's liver disease (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 in some cases, to any other individual interested in the user's health, such as by achieving the recommended treatment.

[0054] In certain embodiments, the output 144 generated by the decision support engine 114 may be stored in a user profile 118. The output 144 may indicate the user's current health, the user's liver status, and / or current treatments recommended to the user. The output 144 stored in the user profile 118 may be continuously updated by the decision support engine 114. Thus, a previous diagnosis initially stored as an output 144 in the user profile 118 in the user database 110 and then passed to the medical history record database 112 may provide an indication of the progression of liver disease in the user over time, as well as an indication as to the effectiveness of different treatments recommended to the user to help halt the progression of the disease.

[0055] In certain embodiments, the user's own medical history data may be used by the training server system 140 to train a personalized model for the user that provides decision support and insights regarding the user's liver disease. For example, the patient's medical history data may be used as a baseline to indicate improvement or deterioration in the patient's liver function. As an illustrative example, the patient's data from one week, two weeks, one month, six months, or one year ago may be used as a baseline that may be compared to the patient's current data to identify whether the patient's liver function has improved or deteriorated. In certain embodiments, the model may further be capable of predicting or estimating the patient's liver function or its future improvement / deterioration based on the user's recent data patterns (e.g., exercise data, food consumption data, medication use data, etc.).

[0056] In certain embodiments, the model may be trained to provide food, exercise, intervention, medication type and dosage, and other types of decision support recommendations to help the user improve their liver function based on the user's medical history data, including how different types of food and / or exercise have affected the user's liver function in the past. For example, if the model is trained to provide food and / or exercise recommendations, the model may be trained by the training server system 140 based at least in part on post-prandial medical history glucose and lactate measurements.

[0057] Generally, monitoring lactate levels over time in a user with liver disease as a measure of liver health may be desirable to provide the user with positive or negative feedback regarding certain lifestyle choices, including exercise, dietary choices, medication types, and dosage recommendations.

[0058] In certain embodiments in which the model is trained to provide therapeutic intervention recommendations, the model may be trained to provide recommendations regarding a particular type of treatment for the user based on the severity of the user's liver disease, historical lactate data such as baseline lactate levels and lactate thresholds, and other analyte or non-analyte sensor data. After the user implements the therapeutic recommendation, the model may continue to monitor the lactate data and other analyte and non-analyte sensor data to determine the impact of the recommended treatment on the user's liver health. Based on data collected following implementation of the therapeutic recommendation, the model may measure liver health over time to provide the user with positive or negative feedback regarding therapeutic intervention.

[0059] In certain embodiments, the model can be trained by the training server system 140 based on historical glucose and lactate to provide recommendations for medication type and dosage. For example, the model can be trained to provide recommendations for medication type and dosage based on the severity of liver disease for the user, historical lactate data such as baseline lactate levels and lactate thresholds, and other historical analyte or non-analyte sensor data. In general, monitoring a user's lactate levels over time can demonstrate the impact of medication type or dosage on the user's liver health. Lactate levels over time can demonstrate, for example, that the user is sensitive to a particular type of statin, and the user can be recommended to use an alternative statin.

[0060] In certain embodiments, the model may be trained to predict the root cause of a particular improvement or deterioration in a patient's liver function. For example, the application 106 may display a user interface with a graph showing the patient's liver function or its score along with a trend line, e.g., retrospectively, showing how the function was affected at a particular point in time.

[0061] In certain embodiments where rules-based models are used to provide decision support, historical glucose and / or lactate measurements may be utilized to determine "healthy" and / or "unhealthy" thresholds or ranges for glucose and / or lactate levels following ingestion of a meal. The healthy and / or unhealthy thresholds or ranges for glucose and / or lactate may then be utilized to inform the user whether the meal was healthy or unhealthy for the user based on the real-time measurements of glucose and / or lactate.

[0062] 2 is a diagram 200 that conceptually illustrates an example continuous analyte monitoring system 104 including an example continuous analyte sensor with sensor electronics according to certain aspects of the present disclosure. For example, the system 104 may be configured to continuously monitor one or more analytes of a user attached to the continuous analyte sensor according to certain aspects of the present disclosure.

[0063] The continuous analyte monitoring system 104 in the illustrated embodiment includes a sensor electronics module 204 and one or more continuous analyte sensors 202 (individually referred to herein as a continuous analyte sensor 202 and collectively referred to herein as continuous analyte sensors 202) associated with the sensor electronics module 204. The sensor electronics module 204 may wirelessly communicate (e.g., directly or indirectly) with one or more of the display devices 210, 220, 230, and 240. In certain embodiments, the sensor electronics module 204 may also wirelessly communicate (e.g., directly or indirectly) with one or more medical devices, such as a medical device 208 (individually referred to herein as a medical device 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 a non-analyte sensor 206 and collectively referred to herein as non-analyte sensors 206).

[0064] 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, or a single analyte sensor configured to continuously measure a single analyte as a non-invasive, subcutaneous, transdermal, transdermal, and / or intravascular device. In certain embodiments, the continuous analyte sensor 202 may be configured to continuously measure an analyte level of a user using one or more measurement techniques, such as enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, radiometric, immunochemical, etc. In certain aspects, the continuous analyte sensor 202 provides a data stream indicative of the concentration of one or more analytes in the user. The data stream may include raw data signals that are then converted to a calibrated and / or filtered data stream used to provide an estimated analyte value to the user.

[0065] In certain embodiments, the continuous analyte sensor 202 may be a multi-analyte sensor configured to continuously measure multiple analytes in the user's body. For example, in certain embodiments, the continuous multi-analyte sensor 202 may be a single multi-analyte sensor configured to measure lactate, glucose, ketones, and / or potassium in the user's body.

[0066] 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 lactate and glucose, and in some cases may be used in combination with an analyte sensor configured to measure only ketones. Information from each of the multi-analyte and single-analyte sensors may be combined to provide liver disease decision support using the methods described herein. In further embodiments, other non-contact and / or periodic or semi-continuous but time-limited measurements of physiological information may be integrated into the system, such as by including weight scale information from a sensor pad under the user while in a chair or bed, or non-contact heart rate monitoring, through an infrared camera that detects the user's temperature and / or blood flow patterns, and / or through a visual camera with machine vision for height, weight, or other parameter estimation without physical contact.

[0067] In certain embodiments, the sensor electronics module 204 includes electronic circuitry associated with measuring and processing the continuous analyte sensor data, including predictive algorithms associated with processing and calibrating the sensor data. The sensor electronics module 204 can be physically connected to the continuous analyte sensor 202 and can be integral (non-releasably attached) or releasably attached to the continuous analyte sensor 202. The sensor electronics module 204 can include hardware, firmware, and / or software that enables measurement of levels of a display device via the continuous analyte sensor 202. For example, the sensor electronics module 204 may include a potentiostat, a power supply for providing power to the sensor, other components useful for signal processing and data storage, and a telemetry module for transmitting data from the sensor electronics module to one or more display devices. The electronics can be affixed to a printed circuit board (PCB) or the like and can take a variety of forms. For example, the electronics can take the form of an integrated circuit (IC), such as an Application-Specific Integrated Circuit (ASIC), a microcontroller, and / or a processor.

[0068] 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, and / or 242, for displaying sensor data to a user and / or accepting input from a user. For example, a graphical user interface (GUI) may be presented to the user for such purposes. In some embodiments, the display device may include other types of user interfaces, such as a voice user interface, instead of or in addition to a 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 a user of FIG. 1 and / or receive input from a user.

[0069] 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.

[0070] The display devices may include custom display devices designed specifically to display a particular type of displayable sensor data associated with the analyte data received from the sensor electronics module. In certain embodiments, the display devices may be configured to provide alerts / alarms 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 smart phone, such as display device 220 representing a mobile phone, configured to display a graphical representation of continuous sensor data (e.g., including current and historical data) using a commercially available operating system (OS). Other display devices may include display device 230 representing a tablet, display device 240 representing a smartwatch, medical device 208 (e.g., an insulin delivery device or a blood glucose meter), and / or a desktop or laptop computer (not shown).

[0071] Because different display devices have different user interfaces, the contents of the data package (e.g., amount, format, and / or type of data displayed, alarms, etc.) can be customized (e.g., programmed differently by the manufacturer and / or end user) for each particular display device. Thus, in certain implementations, multiple different display devices can wirelessly communicate directly with the sensor electronics module (e.g., the on-skin sensor electronics module 204 physically connected to the continuous analyte sensor 202) during a sensor session to enable multiple different types and / or levels of display and / or functionality associated with the displayable sensor data.

[0072] 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 glucose, lactate, and potassium values ​​transmitted from the continuous analyte monitoring system 104, and the continuous analyte sensor 202 is configured to measure glucose, lactate, and / or potassium.

[0073] Additionally, as mentioned, the sensor electronics module 204 may also communicate with other non-analyte sensors 206. The non-analyte sensors 206 may include, but are not limited to, an altimeter sensor, an accelerometer sensor, a temperature sensor, a respiration rate sensor. The non-analyte sensors 206 may also include monitors such as a heart rate monitor, a blood pressure monitor, a pulse oximeter, caloric intake, and a drug delivery device. One or more of these non-analyte sensors 206 may provide data to the decision support engine 114, which is described further below. In some aspects, a user may manually provide a portion of the data for processing by the training server system 140 and / or the decision support engine 114 of FIG. 1.

[0074] In certain embodiments, the non-analyte sensor 206 may be combined in any other configuration, such as, for example, combined with one or more continuous analyte sensors 202. As an illustrative example, a non-analyte sensor, such as a temperature sensor, may be combined with a continuous lactate sensor 202 to form a lactate / temperature sensor that is used to transmit sensor data to the sensor electronics module 204 using a common communication circuit. As another illustrative example, a non-analyte sensor, such as a temperature sensor, may be combined with a multi-analyte sensor 202 configured to measure lactate and glucose to form a lactate / glucose / temperature sensor that is used to transmit sensor data to the sensor electronics module 204 using a common communication circuit.

[0075] 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 and / or cellular connectivity among these devices. Near Field Communication (NFC) and / or Bluetooth may also be used between the devices depicted in the diagram 200 of FIG. 2.

[0076] Figure 3 illustrates example inputs and example metrics calculated based on the inputs for use by the decision support system of Figure 1, according to some embodiments disclosed herein. In particular, Figure 3 provides a more detailed illustration of the example inputs and example metrics introduced in Figure 1.

[0077] 3 illustrates exemplary inputs 128 on the left, applications 106 and DAM 116 in the center, and metrics 130 on the right. In certain embodiments, each of the metrics 130 may correspond to one or more values, such as discrete numeric values, ranges, or qualitative values ​​(such as high / medium / low, or stable / unstable). The applications 106 obtain the inputs 128 via one or more channels (e.g., manual user input, sensors, other applications running on the display device 107, etc.). As mentioned above, in certain embodiments, the inputs 128 may be processed by the DAM 113 to output the metrics 130. 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 used by the decision support engine 114 to diagnose, stage, and assess risk of liver disease.

[0078] In certain embodiments, beginning with input 128, food consumption information may include information about one or more of meals, snacks, and / or beverages, such as one or more of the amount, content (carbohydrates, fat, protein, etc.), order of consumption, and time of consumption. In certain embodiments, food consumption may be provided by the user through manual input, by providing a photo through an application configured to recognize food types and amounts, and / or by scanning a barcode or menu. In various examples, meal amounts may be manually entered as one or more of calories, amount ("3 cookies"), menu items ("Royale with Cheese"), and / or food exchanges (1 fruit, 1 dairy). In some examples, meal information may be received through a convenient user interface provided by application 106.

[0079] In certain embodiments, food consumption, food consumption information (type of food (e.g., liquid or solid, snack or meal, etc.) and / or composition of food (e.g., carbohydrates, fat, protein, etc.)) may be determined automatically based on information provided by one or more sensors. Some exemplary sensors may include body sound sensors (e.g., abdominal sounds may be used to detect type of meal, e.g., liquid / solid meal, snack / meal, etc.), radio frequency sensors, cameras, hyperspectral cameras, and / or analyte (e.g., insulin, glucose, lactate, etc.) sensors for determining food type and / or composition.

[0080] In certain embodiments, the food consumption entered by the user may relate to lactate consumed by the user. Lactate for consumption may include any natural or artificial food or beverage that contains lactate, such as, for example, lactate drinks, yogurt, or whole milk. Lactate for consumption may also include any natural or artificial food or beverage that is converted to lactate when absorbed by the body, such as, for example, fructose drinks. Such lactate consumption may be used by the DAM 116 to calculate the user's lactate clearance rate, as described in more detail with respect to the metrics 130 calculated by the DAM 116.

[0081] In certain embodiments, athletic information is also provided as an input. The athletic information can be any information surrounding an activity that requires physical exertion by the user. For example, the athletic information can range from information related to low intensity (e.g., walking) and high intensity (e.g., sprinting) physical exertion. In certain embodiments, the athletic information can be provided by an accelerometer sensor on a wearable device, such as a watch, fitness tracker, and / or patch. In certain embodiments, the athletic information can also be provided through manual user input and / or through a proxy sensor that measures changes in heart rate (or other cardiac metrics) and a predictive algorithm. 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 certain period of time. This data can be used to train the system to learn about the user's exercise patterns to reduce the need for confirmation questions as time progresses and the training algorithm is optimized. Other analytes 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 hours and days.

[0082] 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 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, for example.

[0083] In certain embodiments, treatment / medication information is also provided as an input. The medication information may include information regarding the type, dosage, and / or timing of when one or more medications are taken by the user. The treatment information may include information regarding different lifestyle habits recommended by the user's physician. For example, the user's physician may recommend that the user drink alcohol in moderation, exercise at least 30 minutes per day, or cut calories by 500-1,000 calories per day to improve liver health. In certain embodiments, the treatment / medication information may be provided through manual user input.

[0084] In certain embodiments, analyte sensor data may also be provided as an input, for example, through the continuous analyte monitoring system 104. In certain embodiments, the analyte sensor data may include lactate data (e.g., a user's lactate value) measured by at least one lactate sensor (or multi-analyte sensor) in the continuous analyte monitoring system 104. In certain embodiments, the analyte sensor data may include glucose data measured by at least one glucose sensor (or multi-analyte sensor) in the continuous analyte monitoring system 104. In certain embodiments, the analyte sensor data may include ketone data measured by at least one ketone sensor (or multi-analyte sensor) in the continuous analyte monitoring system 104. In certain embodiments, the analyte sensor data may include potassium data measured by at least one potassium sensor (or multi-analyte sensor) in the continuous analyte monitoring system 104.

[0085] In certain embodiments, input may also be received from one or more non-analyte sensors, such as the non-analyte sensors 206 described with respect to Figure 2. Input from such non-analyte sensors 206 may include information related to a user's heart rate, respiration rate, oxygen saturation, or temperature (e.g., to detect illness, physical activity, etc.). In certain embodiments, electromagnetic sensors may also detect low power radio frequency (RF) fields emitted from an object or a tool in contact with or in proximity to an object, which may provide information about user activity or location.

[0086] 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 a user input, and / or from an insulin pump. The insulin delivery information may include one or more of insulin amount, delivery time, and the like. Other configurations, such as insulin action time or duration of insulin action, may also be received as input.

[0087] In certain embodiments, the time of day may also be provided as an input, such as a time of day or a time from a real-time clock. For example, in certain embodiments, the input analyte data may be time-stamped to indicate the day and time when the analyte measurement was made for the user. However, in certain embodiments, the time of day may not assist in determining whether the user is asleep or awake. When determining whether the user is asleep or awake, inputs received from non-analyte sensors (e.g., activity monitors), analyte sensors (e.g., lactate or glucose increase), and / or user input may inform the determination of whether the user is asleep or awake.

[0088] User input of any of the inputs 128 referred to above may be through a user interface, such as the user interface of the display device 107 of FIG.

[0089] As described above, in certain embodiments, the DAM 116 determines or calculates metrics 130 for the user based on the inputs 128. An example list of metrics 130 is shown in FIG.

[0090] In certain embodiments, lactate levels may be determined from sensor data (e.g., lactate measurements obtained from a continuous lactate sensor of the continuous analyte monitoring system 104). For example, lactate levels refer to time-stamped lactate measurements or values ​​that are continuously generated and stored over time.

[0091] In certain embodiments, the lactate production rate may be determined from sensor data (e.g., blood glucose measurements obtained from a continuous glucose sensor of the continuous analyte monitoring system 104). In particular, lactate is produced from pyruvate (e.g., glucose is broken down to pyruvate) through the enzyme lactate dehydrogenase during normal metabolism and exercise. In certain embodiments, the lactate production rate may be determined by assessing the increase in lactate levels over a specified amount of time. In certain embodiments, the lactate production rate may be expressed as a percentage of maximum heart rate (e.g., 85% of maximum heart rate) or a percentage of maximum oxygen uptake (e.g., 75%). In certain other embodiments, the lactate production rate may be expressed as a function of accelerometer data. For example, the accelerometer data may indicate a user's step rate over time (e.g., increasing accelerometer data may indicate increasing step rate and vice versa). Each of these step rates may be correlated to the user's lactate level at a specified time. Thus, step rates (e.g., accelerometer data) analyzed over time and their corresponding lactate levels may provide information about the user's lactate production rate relative to the accelerometer data. The DAM 116 may continuously, semi-continuously, or periodically measure the user's lactate production rate over time and store the lactate production rate along with a timestamp in the user's profile 118. The lactate production rate may be time-stamped to allow for identifying decreases or increases in the user's lactate production over time.

[0092] In certain embodiments, the lactate baseline may be determined from sensor data (e.g., lactate measurements obtained from a continuous lactate sensor of the continuous analyte monitoring system 104). The lactate baseline represents the user's normal lactate level during periods when fluctuations in lactate production are typically not expected. A user's baseline lactate is generally expected to remain constant over time unless challenged through actions such as consumption of lactate or lactate metabolizing foods by the user or exercise by the user. Furthermore, each user may have a different lactate baseline. In certain embodiments, a user's lactate baseline may be determined by calculating an average lactate level over a particular amount of time when fluctuations are not expected. For example, a user's baseline lactate may be determined over periods when the user is asleep, sitting in a chair, or other periods when the user is sedentary and not consuming foods or medications that would reduce or increase lactate levels. In certain embodiments, the DAM 116 may continuously, semi-continuously, or periodically calculate the lactate baseline and timestamps and store the corresponding information in the user's profile 118. In certain embodiments, the DAM 116 may calculate the lactate baseline using lactate levels measured over a period of time during which the user is sedentary, the user is not consuming lactate, and there are no external conditions that affect the lactate baseline. In certain other embodiments, the DAM 116 may use lactate levels measured over a period of time during which the user is engaged in exercise and / or consuming lactate, and / or there are external conditions that affect the lactate baseline, for at least a subset of the period. In this case, in some examples, the DAM 116 may identify which measured lactate values ​​should be used to calculate the baseline lactate by first identifying lactate values ​​that may have been affected by external events such as food intake, exercise, medication, or other perturbations that prevent the capture of lactate baseline measurements. The DAM 116 may exclude such measurements when calculating the user's lactate baseline.In some other embodiments, the DAM 116 may calculate the lactate baseline by first determining the percentage of the number of lactate values ​​measured during this period that represents the lowest lactate value measured. The DAM 116 may then take the average of this percentage to determine the lactate baseline level.

[0093] In certain embodiments, the lactate clearance rate may be determined from sensor data (e.g., lactate measurements obtained from a continuous lactate sensor of the continuous analyte monitoring system 104). In particular, the lactate clearance rate of a user indicates the rate at which lactate metabolism is greater than lactate production. The lactate clearance rate may be indicative of liver function (e.g., the slope of the lactate clearance curve may indicate liver function). In certain embodiments, the lactate clearance rate may be determined by calculating the slope between an initial lactate value (e.g., during the lactate level increase phase) and a lactate baseline associated with the user. In certain embodiments, the lactate clearance rate may be calculated over time until the user's lactate level reaches some value relative to the user's lactate baseline (e.g., 50% or 75% of the lactate baseline). In certain embodiments, the lactate clearance rate may be calculated over time until the user's lactate level reaches a value relative to the peak lactate level measured for the user at a previous time (e.g., the user's lactate level reaches 25%, 50%, and / or 75% of the user's peak lactate level).

[0094] Furthermore, monitoring lactate clearance rate after exercise or after a meal may demonstrate improvement or progression of liver disease. Because progression of liver disease is typically slow, monitoring lactate clearance rate over time (e.g., after exercise or after a meal) and comparing the current lactate clearance rate to past lactate clearance rates (after exercise or after a meal) may be useful in determining disease progression over time. For example, if the lactate clearance rate significantly slows or trends worse over time (e.g., lactate clearance rate is X at time Z and lactate clearance rate is XY at some time Q in the future), liver disease is progressing to a more severe state. In some embodiments, the decision support engine 114 provides daily updates to the user as the lactate clearance rate is continuously monitored.

[0095] In certain embodiments, the lactate clearance rate may be expressed as a function of the user's lactate half-life. In particular, there is an inverse correlation between lactate clearance rate and lactate half-life. In diseased livers, the slope of lactate clearance decreases as the calculated lactate half-life increases. As liver disease progresses, the slope of lactate clearance decreases further and the calculated lactate half-life increases further. Thus, the lactate half-life may be indicative of the user's lactate clearance rate. The calculated lactate clearance rates over time may be time-stamped and stored in the user's profile 118.

[0096] In certain embodiments, the classifier can be used to determine whether the data corresponds to a perturbation in the rate of lactate increase or decrease followed by a sudden increase or decrease in the user's lactate in a different direction (e.g., resulting in what would otherwise be an unexpected change in the slope of the user's lactate clearance). These situations can include a user being sedentary, then starting to exercise, stopping for a short period of time, then starting to exercise again. In this case, the lactate production rate may not be constant as with a constant rate of clearance, since the increase in lactate produced during exercise is proportional to the time-dependence of the length of the exercise. Similarly, this can occur when a patient undergoing lactate measurements on a fasting diet consumes edible substances that increase the rate of lactate production in a consistent or contradictory manner (e.g., depending on whether the consumed substance is homogeneous or heterogeneous). In heterogeneous substances, the changes in lactate levels may be heterogeneous, since certain foods are digested at different rates based on the differences between the sugars, proteins, and fats of each of these foods. The data classifier system will aid in the inclusion and / or exclusion of relevant lactate production data (true rates increasing) from lactate clearance data (true rates decreasing). Additionally, the data classifier system will aid in determining areas of variably elevated lactate or otherwise rapidly changing data in proportion to the regulation of lactate production from the consumption balance from overlapping increasing and decreasing signals.

[0097] In certain embodiments, lactate trend can be determined based on lactate levels over a certain period of time. In certain embodiments, lactate trend can be determined based on lactate production rate over a certain period of time. In certain embodiments, lactate trend can be determined based on calculated lactate clearance rate over a certain period of time.

[0098] In certain embodiments, glucose levels may be determined from sensor data (e.g., blood glucose measurements obtained from a continuous lactate sensor of the continuous analyte monitoring system 104). Elevated glucose levels may be used in combination with lactate levels to determine whether the user has consumed a meal. To more accurately determine that the user has consumed a meal, elevated glucose and lactate levels may also be combined with a body sound sensor, as described above, to confirm that the user has consumed a meal. Furthermore, if glucose levels are combined with data from an activity monitor, high lactate and high glucose levels may indicate high intensity exercise as opposed to consumption of a meal. Conversely, lower glucose levels combined with high lactate levels may demonstrate that the high lactate levels are due to a condition other than a meal (e.g., poor liver health, infection, or exercise).

[0099] In certain embodiments, blood glucose trends may be determined based on glucose levels over a particular period of time.

[0100] In certain embodiments, insulin sensitivity may be determined using medical history 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 consumption 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.

[0101] 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 usage driven by activity or food consumption.

[0102] In certain embodiments, insulin and / or glucose sensor data (or derived values) may be used in combination with lactate sensor data to create correction factors for certain activities (e.g., variations in anaerobic vs. glucose metabolic rates, etc.). This may be particularly important in diabetic people who are receiving insulin, who often have high levels of circulating insulin, with high levels of glycolysis resulting in excess pyruvate that can be converted to glucose. Thus, there is a complementary and inverse correlation between glucose, insulin, and / or lactate levels that may inform the health status of a patient or may be useful in diagnosing the state of liver health.

[0103] Additionally, diagnosing the stage of liver disease over multiple sessions over many months may be a useful tool for sequentially determining disease progression over time (e.g., as user behavior or underlying health conditions result in reversal or progression of liver disease). Such disease progression / reversal information may be shared in displays to patients, caregivers, family, health insurance companies, or other stakeholders interested in the patient's disease status over a longer period of time. Additionally, acute events such as hepatic decompensation and hypoglycemia in liver disease users may be identified before severe acute symptoms become overwhelmingly debilitating by measuring the rate of change and absolute levels of insulin, glucose, and lactate in the user.

[0104] In certain embodiments, ketone levels may be determined from sensor data (e.g., ketone measurements obtained from continuous analyte monitoring system 104). In certain embodiments, ketone levels may be expressed as a metric of whether a user is in ketosis. In particular, ketosis is a metabolic state in which high concentrations of ketones are present in the user's blood.

[0105] In certain embodiments, the ketone production rate may be determined from sensor data (e.g., ketone measurements obtained from the continuous analyte monitoring system 104). In particular, ketones (chemically known as ketone bodies) are a by-product of the breakdown of fatty acids. Glucose (e.g., blood glucose) is the preferred fuel source for many cells in the body; however, when cells have limited access to glucose, fat may be broken down for fuel, thereby producing ketones as a by-product. In certain embodiments, the ketone production rate may be determined by assessing the increase in ketone levels over a specified amount of time.

[0106] In certain embodiments, the potassium level may be determined from sensor data (eg, potassium measurements obtained from the continuous analyte monitoring system 104).

[0107] In certain embodiments, the health and illness metrics may be determined, for example, from a physiological sensor (e.g., temperature), an activity sensor, or a combination thereof, based on one or more of user inputs (e.g., pregnancy information or known illness information). In certain embodiments, based on the values ​​of the health and illness metrics, for example, the user's condition may be defined as one or more of healthy, sick, rest, or fatigued.

[0108] In certain embodiments, disease stage metrics, such as for liver disease, may be determined based on one or more of user inputs or outputs provided by the decision support engine 114 illustrated in FIG. 1, for example. In certain embodiments, exemplary disease stages of liver disease may include an inflammatory stage (e.g., an early stage in which the user's liver is enlarged or inflamed), a fibrotic stage (e.g., a stage with signs of scar tissue in an inflamed liver), a cirrhotic stage (e.g., a stage with signs of severe scar tissue in an inflamed liver), end-stage liver disease (ESLD). In certain embodiments, exemplary disease stages may be represented as NASH scores, NAFLD fibrosis scores, Child-Pugh scores, model for ESLD (MELD) scores, meta-analysis of histological data in viral hepatitis (METAVIR) scores, and the like. Additionally, hepatocellular carcinoma may often be present throughout the later stages of cirrhosis and / or ESLD.

[0109] In certain embodiments, the decision support engine 114 may use the MELD score (or other liver disease metric / score) in combination with lactate data (or other analyte data) to predict liver disease progression and liver decompensation. Using the MELD score in combination with lactate data may be more effective or predictive than the MELD score alone or lactate data alone. For example, the MELD score alone may indicate liver disease, but the rate of increase in lactate levels outside of diet or exercise correlates with the severity of the condition (e.g., liver damage or other systemic or organ damage). A higher rate of change in resting lactate compared to historical lactate rate of change, in combination with the patient's MELD score, may indicate more severe liver dysfunction. In other cases, a higher rate of change in resting lactate compared to historical lactate rate of change may indicate other health conditions.

[0110] In certain embodiments, the meal state metric may indicate the user's state with respect to food consumption. 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 nutrition, e.g., meals, snacks, or beverages consumed, and may be determined from food consumption information, mealtime information, and / or digestibility information, which may be correlated, for example, to food type, amount, and / or order (e.g., which food / beverage was eaten first). The meal state metric may be determined automatically based on information provided through user input or based on information provided by one or more sensors (e.g., a body sound sensor as described above).

[0111] In certain embodiments, the eating habits metric is based on the content and timing of the user's meals. For example, if the eating habits metric is on a scale of 0 to 1, in an example, the better / healthier the diet the user eats, the higher the user's eating habits metric will be relative to 1. Also, in this example, the more the user's food consumption adheres to a particular time schedule, the closer their eating habits metric will be to 1. In certain embodiments, the eating habits metric may indicate whether the user is consistently engaging in a ketogenic diet (e.g., a low carbohydrate, moderate protein, and high fat diet) based on the meals, snacks, or beverages consumed by the user over a period of time.

[0112] In certain embodiments, medication adherence 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 taking 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 in a clinical trial in which medication consumption and the timing of such medication consumption are monitored.

[0113] In certain embodiments, the activity level metric may indicate a user's activity level. In certain embodiments, the activity level metric is determined based on input from an activity sensor or other physiological sensor, such as, for example, the 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 motion 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 may be contemporaneously correlated with the user's lactate level.

[0114] In certain embodiments, the exercise regimen metric may indicate one or more of what type of activity the user engages in, 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 non-analyte sensor data inputs (e.g., non-analyte sensor data inputs from an accelerometer, heart rate monitor, respiration rate sensor, etc.), calendar inputs, user inputs, etc.

[0115] In certain embodiments, metabolic rate is a metric that may be indicative of or include basal metabolic rate (e.g., energy expended at rest) and / or active metabolism (e.g., energy expended by activity such as physical exertion). In some examples, basal metabolic rate and active metabolism may be tracked as separate outcome metrics. In certain embodiments, metabolic rate may be calculated by DAM 113 based on one or more of inputs 128, such as one or more of exercise information, non-analyte sensor data, time, etc.

[0116] In certain embodiments, a body temperature metric may be calculated by the DAM 113 based on the input 128, more specifically, non-analyte sensor data from a temperature sensor. In certain embodiments, a heart rate metric may be calculated by the DAM 113 based on the input 128, more specifically, non-analyte sensor data from a heart rate sensor. In certain embodiments, a respiration metric may be calculated by the DAM 113 based on the input 128, more specifically, non-analyte sensor data from a respiration rate sensor.

[0117] Exemplary methods and systems for diagnosing, staging, treating, and assessing risk of liver disease using monitored analyte data 4 is a flow diagram illustrating an exemplary method 400 for providing decision support using a continuous analyte sensor, including at least a continuous lactate sensor, according to certain exemplary embodiments of the present disclosure. For example, the method 400 may be performed to provide decision support to a user using a continuous analyte monitoring system 104, including at least a continuous lactate sensor 202, as illustrated in FIGS. 1 and 2. The method 400 may be performed by the decision support system 100, for example, to collect data including the analyte data, patient information, and non-analyte sensor data described above, to (1) automatically detect and classify abnormal liver conditions, (2) assess the presence and severity of liver disease, (3) risk stratify patients to identify patients at high risk for liver disease, (4) identify risks associated with a current liver disease diagnosis (e.g., risk of death, risk of liver cancer, etc.), (5) make patient-specific treatment decisions or recommendations for liver disease, and (6) provide information regarding the effectiveness of an intervention (e.g., the effectiveness of a patient's lifestyle changes, the effectiveness of a surgical procedure, the effectiveness of a patient taking a new medication, etc.). In other words, the decision support system presented herein may provide information useful in directing and improving care for patients with or at risk for liver disease. Method 400 is described below with reference to Figures 1 and 2 and their components.

[0118] At block 402, the method 400 begins by continuously monitoring one or more analytes, including at least lactate, of a patient, such as the user 102 illustrated in Figure 1, during a first time period to obtain analyte data. Block 402 may be performed in certain embodiments by the continuous analyte monitoring system 104 illustrated in Figures 1 and 2, and more specifically, the continuous analyte sensor 202 illustrated in Figure 2. For example, the continuous analyte monitoring system 104 may include the continuous lactate sensor 202 configured to measure the lactate level of the user.

[0119] Lactate is the conjugate base of lactic acid. Lactate is produced from pyruvate (e.g., glucose is broken down to pyruvate) through the enzyme lactate dehydrogenase during normal metabolism and exercise. Approximately 70% of lactate is metabolized by the liver. However, in very early liver disease, such as NAFLD, lactate metabolism is altered, which can lead to elevated lactate levels in the body. Furthermore, as liver disease progresses, lactate production rate further increases, lactate metabolism is impaired, and lactate half-life increases. Lactate elevation can be caused by such increased production, decreased clearance, or a combination of both. Therefore, lactate may need to be continuously monitored to continuously assess parameters such as lactate clearance rate (also showing lactate half-life), lactate level, lactate production rate, and lactate baseline, in order to diagnose, stage, treat, and assess the risk of liver disease in real time.

[0120] The primary analyte for the measurements described herein is lactate, although in certain embodiments other analytes may be considered. In particular, combining lactate measurements with additional analyte data may help to further inform analysis related to liver disease diagnosis and staging. For example, monitoring additional types of analytes, such as glucose, ketones, and / or potassium, as measured by the continuous analyte monitoring system 104, may provide additional insight into liver disease diagnosis and / or supplemental information used to determine optimal treatments for preventing disease progression (and in some cases, for disease regression).

[0121] The additional insight gained from using a combination of analytes, rather than just lactate, may increase the accuracy of liver disease diagnosis. For example, the probability of correctly diagnosing and / or staging liver disease may be a function of the number of analytes measured for a user. More specifically, in some embodiments, the probability of correctly staging liver disease using only lactate data (in addition to other non-analyte data) may be lower than the probability of correctly staging liver disease using lactate and glucose data (in addition to other non-analyte data), which may also be lower than the probability of correctly staging liver disease using lactate, glucose, ketone, and potassium data (in addition to other non-analyte data) for analysis.

[0122] In certain embodiments described herein, for example, for staging liver disease, the combination of analytes measured and collected by one or more (e.g., multi-analyte) sensors includes lactate and at least one of glucose, ketones, or potassium; however, other analyte combinations may be considered for diagnosing and staging liver disease.

[0123] For example, in certain embodiments, at block 402, the continuous analyte monitoring system 104 may continuously monitor the user's glucose level during a first period of time. In some embodiments, glucose levels may be monitored in conjunction with or instead of other analytes (e.g., lactate, ketones, etc.). In such embodiments, the measured glucose concentration may be used to further inform analyses for diagnosing and staging liver disease. In particular, in some cases, glucose levels are indicative of the likelihood of developing insulin resistance and / or type II diabetes (T2D), which conditions increase the risk of liver disease.

[0124] For example, during digestion, carbohydrate-containing food is converted into glucose, which is then sent to the bloodstream, causing an increase in blood glucose levels. This increase in blood glucose generally signals the pancreas to produce insulin. Insulin mediates the precise regulation of glucose metabolism and plasma concentration, not only by promoting glucose uptake by skeletal muscle, liver, and adipose tissue, but also by suppressing hepatic glucose production. Insulin plays a key role in lipid metabolism by binding to its receptor to promote fatty acid esterification, promoting fatty acid storage in lipid droplets, and inhibiting lipolysis. However, under conditions of insulin resistance, cells in muscles, liver, and tissues do not respond well to insulin and cannot use glucose in the blood for energy. In response, the pancreas is stimulated to increase insulin secretion, resulting in higher insulin levels in the liver and high concentrations of glucose in the blood. High concentrations of insulin can affect enzymes in the body that result in an increase in free fatty acids (FFAs) that can enter the liver. An increase in FFAs can lead to an excess amount of fat stored in liver cells, and in some cases, NAFLD. In other words, patients with insulin resistance, typically seen in patients with T2D, may be at higher risk of developing NAFLD. Furthermore, since T2D is a disease that can cause deterioration of liver function, continuous glucose measurements may indicate the likelihood or state of T2D, which may predict liver disease and / or NAFLD. Glucose metrics that may be used include glucose basic statistics (e.g., mean median, variance, interquartile range, etc.), glucose time in range, glucose peak metrics (e.g., peak count, frequency, width, etc.), autocorrelation-related metrics (e.g., correlation coefficient, lag, etc.), and / or frequency domain metrics (e.g., peak frequency, width of frequency peak, etc.). Thus, monitoring a user's glucose level may help inform an assessment of the user's likelihood of developing liver disease.

[0125] Furthermore, liver disease and liver dysfunction may result in frequent hyperglycemia, particularly after meals, as described above.Patients with liver disease may also experience nocturnal hypoglycemia as liver disease progresses.Therefore, frequent postprandial hyperglycemia and nocturnal hypoglycemia, in combination with lactate measurements (e.g., higher baseline or resting lactate levels, postprandial lactate levels, and impaired lactate clearance rate), may provide a more complete prediction of liver disease improvement or progression, and / or inform the user's assessment of the likelihood of developing liver disease.

[0126] In some embodiments, there is a complementary and inverse correlation between glucose and lactate levels that may inform the health status of a patient or may be useful in diagnosing the state of liver health. For example, in a healthy user, lactate and glucose trends may be closely correlated (e.g., lactate and glucose levels may peak at similar times in response to events such as exercise or food consumption). Thus, divergence of lactate and glucose trends may indicate renal or hepatic dysfunction in the user. For example, a larger and / or delayed lactate peak compared to a glucose peak may indicate that the user has progressive liver disease or poor metabolic fitness, as the patient's body may not be able to effectively remove the substrate and / or may not be able to effectively switch between removing lactate and glucose.

[0127] In another example, at block 402, the continuous analyte monitoring system 104 may continuously monitor the user's ketone levels during a first period of time. In some embodiments, ketone levels may be monitored in conjunction with or instead of other analytes (e.g., glucose, lactate, etc.). In such embodiments, the measured ketone concentrations may be used to diagnose, stage, assess risk of liver disease, and / or assess different treatments for liver disease. For example, in some cases, ketone-specific metrics may aid in the recommendation of specific diets for a user diagnosed with liver disease and may further provide real-time feedback regarding improvement in liver dysfunction following implementation of the recommended diet.

[0128] In some cases, where a user has previously been diagnosed with NAFLD, the user's measured ketone levels, for example, measured continuously using the continuous ketone sensor 202, may inform treatment recommendations for the disease. For example, in some cases, based on the user's ketone levels, a ketogenic diet may be recommended to the user. A ketogenic diet essentially aims to get the body to use a different type of fuel. Instead of relying on sugars (e.g., glucose) derived from carbohydrates (such as grains, legumes, vegetables, and fruits), a ketogenic diet relies on ketone bodies, namely acetoacetate, acetone, and β-hydroxybutyrate (βHB), a type of fuel that the liver produces from stored fat. A ketogenic diet may be used to put the user's body into ketosis (e.g., a metabolic state in which there are high concentrations of ketones in the blood) to ultimately reverse the effects of NAFLD. For a user diagnosed with fatty liver disease (e.g., NAFLD), consuming more fat may seem counterintuitive. However, putting the user's body into ketosis induces the body to begin burning body fat in addition to dietary fat. This can ultimately help improve the user's liver health as the user's body begins to eradicate the very problem that is causing fatty liver. The improvements to the liver, in this case, can have a direct correlation with the increase in ketone levels in the user, for example, due to the implementation of a ketogenic diet.

[0129] In yet another example, the continuous analyte monitoring system 104 may continuously monitor a combination of two or more of the user's lactate, glucose, and ketones during a first time period in block 402. In such an embodiment, the measured concentrations are used to further inform analyses for diagnosing and staging liver disease.

[0130] In particular, in certain embodiments, as mentioned above, insulin mediates the precise regulation of glucose metabolism and plasma concentration by promoting glucose uptake by skeletal muscle, liver, and adipose tissue. Thus, when insulin is low, glucose is limited for uptake by skeletal muscle, liver, and adipose tissue. Such limited access to glucose by at least the liver causes the liver to break down fat for fuel instead (e.g., ketone production). Given that ketones (e.g., ketone bodies) are by-products of the breakdown of fatty acids, when the liver breaks down such acids, high concentrations of ketones in the blood may be expected. However, if the user has been diagnosed with liver disease, the liver may have an impaired ability to produce ketones (e.g., impaired ketone production), and therefore ketone concentrations may not be as high as expected.

[0131] In some cases, this may lead a user to believe that they are in a healthy state (e.g., if the bloodstream is flooded with extremely high levels of ketones) when in fact the user is suffering from diabetic ketoacidosis (DKA). Given that the expected ketone levels for a user with DKA are reduced by the liver's impaired ability to produce such ketones (e.g., masked by liver disease), the user may not realize that they have DKA. Thus, low insulin and high blood glucose levels combined with a user's low ketone levels may be good indicators of liver dysfunction to inform a diagnosis. This indicator, combined with the user's continuously measured lactate levels, may help to increase the accuracy of predicting the presence and / or severity of liver disease in the user. Conversely, low blood glucose combined with high ketone levels may indicate that the user is experiencing ketosis, which is often due to a ketogenic diet. A ketogenic diet may improve liver health over time, and as a result, may also reduce the user's lactate levels. Therefore, a ketogenic diet may be recommended to users with liver disease to improve their liver health over time.

[0132] In another example, in block 402, the continuous analyte monitoring system 104 may continuously monitor the user's potassium levels during a first period of time. In some embodiments, potassium levels may be monitored in conjunction with or instead of other analytes (e.g., glucose, lactate, ketones, etc.). Measuring potassium may help inform the diagnosis and staging of liver disease, as reduced potassium excretion may correlate with fatty liver disease. Alternatively, increased potassium excretion may be associated with chronic liver failure, and increased lactate may provide additional confirmation of a liver failure diagnosis, particularly in examples where the user also suffers from acute kidney injury. Acute kidney injury is a common complication for patients suffering from liver failure or cirrhosis. Thus, increased lactate and potassium levels may indicate that the user is suffering from acute kidney injury, which may correlate with liver failure. However, if potassium levels remain stable and lactate levels increase, kidney damage is likely not a contributing factor or cause of liver failure. In other examples, hepatic uptake of potassium in response to insulin may be impaired, causing hyperkalemia. In this case, hyperkalemia may occur independent of acute kidney injury, and therefore combining potassium measurements with lactate, ketone, and / or glucose measurements may provide a more accurate diagnosis of liver disease.

[0133] In certain embodiments, AI models, such as machine learning models and / or algorithms, may be used to provide real-time decision support for the diagnosis and staging of liver disease. In certain embodiments, such models may be configured to use input from one or more sensors measuring multiple analyte data to diagnose, stage, treat, and assess the risk of liver disease. Thus, taking into account the interaction of such comorbidities (e.g., as shown with respect to an example for a user with DKA and liver disease), 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 input to reflect knowledge gained from each of the other analytes being measured / prevalence associated with the additional analytes being measured.

[0134] In addition to continuously monitoring one or more analytes of the user during the first time period to obtain analyte data at block 402, optionally in certain embodiments, method 400 may also include monitoring other data during the first time period using one or more non-analyte sensors or devices at block 404. Block 404 may be performed in certain embodiments by non-analyte sensors 206 and / or medical device 208 of FIG.

[0135] As mentioned above, the non-analyte sensors and devices may include, but are not limited to, one or more of an insulin pump, a respiration sensor, a heart rate monitor, an accelerometer, a sensor or device provided by the display device 107 (e.g., an accelerometer, a camera, a global positioning system (GPS), etc.), or any other sensor or device that provides relevant information about the user. Metrics, such as the metrics 130 illustrated in FIG. 3, may be calculated using measurement data from each of these additional sensors. As further illustrated in FIG. 3, the metrics 130 calculated from the non-analyte sensor or device data may include metabolic rate, body temperature, heart rate, respiration rate, etc. In certain embodiments, as described in more detail below, the metrics 130 calculated from the non-analyte sensor or device data may be used to further inform an analysis for diagnosing and / or staging liver disease.

[0136] In certain embodiments, one or more non-analyte sensors and / or devices may be worn by the user to aid in detecting periods of increased physical exertion by the user. Such non-analyte sensors and / or devices may include accelerometers, electrocardiogram (ECG) sensors, blood pressure sensors, heart rate monitors, and the like. In certain embodiments, data measured and collected from periods of increased physical exertion and periods of sedentary activity by the user may be used to analyze at least renal, cardiac, skeletal muscle, and / or hepatic function during each of these identified periods. In particular, up to about 70% of lactate is removed by the liver during periods of sedentary activity by the user, with contributions from the kidneys, heart, and skeletal muscle. The amount of lactate removed by the liver may be less than 70% during periods of physical exertion by the user (e.g., due to additional lactate removed from skeletal muscle and heart). Thus, in certain embodiments, data measured and collected from periods of increased physical exertion and periods of sedentary activity by a user can be used to understand the lactate clearance performed by the liver, kidneys, heart, and / or muscles during each of these identified periods. As described in more detail below, understanding the percentage of lactate clearance performed by different organs of the body can help isolate the lactate clearance performed by the liver alone to better understand liver function and any disorders that may be present, and inform the liver diagnosis and staging techniques described herein.

[0137] At block 406, the method 400 continues by processing the analyte data from the first time period to determine at least one lactate clearance rate. Block 406 may be performed by the decision support engine 114 in certain embodiments. As mentioned, even in very early liver disease such as NAFLD, the metabolism of lactate by the liver is impaired, and therefore lactate has a longer half-life (compared to lactate half-life in healthy individuals). Thus, lactate clearance rate (and lactate level) may provide necessary information regarding liver health and / or stage of liver disease. Certain operations described herein with respect to method 400 involve calculating a lactate clearance rate (e.g., block 406) and / or using the lactate clearance rate to generate a disease prediction (e.g., block 414), but may include calculating one or more other lactate-derived metrics (e.g., area under the lactate curve, lactate baseline, lactate rate of change, postprandial lactate, time above a specified lactate range (e.g., 2 mmol), time below a specified lactate range (e.g., 2 mmol)) instead of, or in addition to, the lactate clearance rate. , median lactate level, number of instances where lactate is above or below a specified value, amount of time lactate levels are within a particular range (e.g., 0.5 mmol to 1.5 mmol), average or median rate of change in lactate over a particular period (e.g., over 24 hours), number of times the rate of lactate change (absolute value) is above a specified value, and / or information regarding these values ​​during exercise or inactivity) may be calculated as well and used to generate disease predictions or generate treatment recommendations, as discussed with respect to blocks 414 and 416. Note that lactate area under the curve refers to the area on a graph between a lactate curve (e.g., a display of continuous lactate measurements plotted on a graph with respect to time) and the time axis, where time is measured on the X-axis and lactate is measured on the Y-axis.

[0138] In general, the slope of lactate clearance is calculated by analysis of lactate measurements over time from a peak value, either (1) after exercise or (2) after lactate consumption, to a baseline value of lactate (e.g., which may vary between different users and within a user based on different times of the day (e.g., a user's morning vs. afternoon vs. evening baseline values ​​of lactate)), to some value relative to the baseline (e.g., 50% or 75% of the baseline), or to some value relative to the peak value (e.g., 25%, 50%, or 75% of the peak value). The lactate clearance rate calculated in the above manner may correspond to the total lactate clearance performed by the liver, kidneys, heart, and / or skeletal muscle.

[0139] Additionally, the level of lactate increase over time (e.g., the change in peak value and / or the rate of lactate increase over time) either (1) after exercise or (2) after lactate consumption (e.g., as part of a meal) may indicate impaired liver function. For example, if the peak lactate value or the rate of lactate increase after lactate consumption increases over time, the user may experience a deterioration in liver health and / or function.

[0140] Separation of hepatic lactate clearance from the calculated lactate clearance may be desirable to classify abnormal liver conditions, however, hepatic lactate clearance separation may present a complex challenge, in particular, hepatic lactate clearance may be different for each user being analyzed, and furthermore, may be different during different periods of physical exertion and / or inactivity for each user, considering that the user's kidneys, heart, and liver also play important roles in removing lactate from the body.

[0141] Techniques for isolating the lactate clearance performed by the liver are provided herein. In particular, in certain embodiments, a method 400 for determining at least one lactate clearance rate includes identifying at least one lactate increase period of a user during at least a first period in block 408, calculating a first lactate clearance rate of the patient after the at least one lactate increase period in block 410, and correcting the first lactate clearance rate of the patient to isolate the lactate clearance by the liver of the patient in block 412. Blocks 408, 410, and 412 of FIG. 4 can be better understood with reference to the workflow 500 of FIG. 5.

[0142] FIG. 5 is an exemplary workflow 500 for isolating hepatic lactate clearance rate using at least a continuous lactate monitor, according to certain embodiments of the present disclosure.

[0143] The workflow 500 of FIG. 5 may be executed by the decision support system 100, which includes the decision support engine 114. As shown in FIG. 5, the workflow 500 begins in block 408 by the decision support engine 114 identifying at least one period of increased lactate during at least a first time period during which one or more of the user's analytes are continuously monitored to obtain analyte data. As an illustrative example, assuming that a user wears a continuous analyte sensor 202 for continuously measuring lactate over a period of time, such as a 24-hour period, the decision support engine 114 may identify periods of increased lactate concentration for the user during this 24-hour period. In this example, it may be determined that the user experienced peak lactate levels between 9:00 AM-10:00 AM and 1:00 PM-1:30 PM (determined based on the user's continuously measured lactate).

[0144] In block 410, the decision support engine 114 calculates a first lactate clearance rate for the user after at least one period of increased lactate. Using the example above, the decision support system 100 calculates a first lactate clearance rate for the user after an identified period of high lactate levels from 9:00 AM to 10:00 AM, and another first lactate clearance rate for the user after an identified period of high lactate levels from 1:00 PM to 1:30 PM.

[0145] In particular, in block 506, the decision support engine 114 determines the user's maximum lactate level during at least one lactate increase period. In block 508, the decision support system 100 determines the amount of time it takes for the user's maximum lactate level to decrease to a percentage of the user's baseline lactate level after at least one lactate increase period. In some cases, the user's baseline lactate level may be the user's baseline lactate level immediately prior to the increase in the user's lactate level. In some cases, the user's baseline lactate level may be the user's baseline lactate level calculated as an average over a specified time range. For example, the user's baseline lactate level may be calculated as an average over one or more days, such as the user's morning lactate level, the user's afternoon lactate level, the user's evening lactate level, etc. In certain embodiments, the user's baseline lactate level may be the user's fasting baseline lactate level. Although the exemplary embodiment of FIG. 4 illustrates the decision support system 100 determining in block 508 the amount of time it takes for the user's maximum lactate level to decrease to a percentage of the user's baseline lactate level, in certain other embodiments, the decision support system 100 determines the amount of time it takes for the user's maximum lactate level to decrease to a percentage of the user's maximum lactate level (e.g., 25%, 50%, and / or 75% of the maximum lactate level). In certain embodiments, one or more of these slopes may be analyzed and compared for analysis. In block 510, the decision support engine 114 calculates the user's first lactate clearance rate using the first lactate level determined in block 506 and the amount of time determined in block 508.

[0146] For example, in block 506, the decision support engine 114 determines the user's maximum lactate levels during identified periods of high lactate concentrations throughout a 24 hour period, e.g., a first period between 9:00 AM and 10:00 AM and a second period between 1:00 PM and 1:30 PM, by analyzing the lactate data collected during those periods. In this example, it may be assumed that the decision support engine 114 determines a maximum lactate level of 8 mmol / L between 9:00 AM and 10:00 AM and a maximum lactate level of 5 mmol / L between 1:00 PM and 1:30 PM.

[0147] At block 508, the decision support engine 114 determines the amount of time it takes for the peak lactate values ​​of each of the two identified time periods to decrease to the user's baseline lactate level. As mentioned with respect to FIG. 3, the baseline lactate level may indicate the user's normal lactate value while the user is at rest (e.g., sedentary). Assuming the user's baseline lactate level is 2 mmol / L, the decision support system 100 may determine the amount of time it takes for the measured lactate level to reach 2 mmol / L after peak lactate concentrations of 8 mmol / L and 5 mmol / L.

[0148] At block 510, the decision support engine 114 calculates a first lactate clearance rate for the user using the peak lactate concentration of 8 mmol / L determined at block 506 and the amount of time determined at block 508. Additionally, the decision support engine 114 calculates a second lactate clearance rate for the user for a second period of time using the peak lactate concentration of 5 mmol / L determined at block 506 and the amount of time determined at block 508. In other words, the decision support engine 114 calculates the slope of lactate clearance over time from each of the identified peak lactate values.

[0149] In other embodiments, the first lactate clearance can be determined as part of the user's first area under the lactate curve for the first time period using the peak lactate concentration of 8 mmol / L determined in block 506 and the amount of time determined in block 508. The area under the curve can be calculated using the peak lactate concentration and the time from the lactate baseline before the lactate peak to the lactate return to the baseline after the lactate peak. The lactate clearance can be useful in determining the rate of lactate return to the baseline after the lactate peak and therefore can be useful in the area under the curve calculation. The area under the lactate curve for the first time period is compared to the area under the lactate curve for the second time period. Additionally, the decision support engine 114 calculates the user's second area under the lactate curve for the second time period using the peak lactate concentration of 5 mmol / L determined in block 506 and the amount of time determined in block 508. In other words, the decision support engine 114 may calculate the slope of the lactate area over time from each of the identified peak lactate values.

[0150] The first lactate clearance rate calculated in block 504 may indicate the total lactate removed by the user's liver, kidneys, heart, and / or skeletal muscles. To isolate the lactate clearance performed by the user's liver, the decision support engine 114 corrects the user's first lactate clearance rate in 412 to isolate the lactate clearance by the user's liver. Thus, the embodiments described herein provide a technical solution to the technical problem described above by correcting the lactate clearance rate to isolate the lactate clearance by the user's liver. For example, the decision support engine 114 performs the steps in blocks 512-524 of FIG. 5 to correct the lactate clearance rate to accurately isolate the lactate clearance rate by the liver and thus more accurately generate liver disease predictions.

[0151] In block 512, the decision support engine 114 determines whether the at least one identified lactate increase period was caused by the user's physical exertion. The user's lactate level increases when the user exercises, thereby reducing blood and oxygen flow throughout the body, or when the user consumes lactate (e.g., yogurt or Cytomax). The percentage of lactate clearance performed by each of the user's liver, kidneys, heart, and / or skeletal muscles may be different for each of several different scenarios. For example, in a scenario where the user is engaged in exercise (e.g., a higher level of physical exertion) and begins a cool-off period (e.g., involving light exercise such as walking), the user's muscles may still be actively reducing lactate. Alternatively, in a scenario where the user is sedentary and consumes a lactate drink (e.g., milk), the user's muscles may not be actively reducing lactate. Thus, a greater percentage of the calculated lactate clearance may be removed by the liver in a scenario where the user is sedentary and ingests a lactate drink, as opposed to a scenario where the user is in a cool-off period after increased physical exertion (e.g., in this scenario, a given muscle is responsible for a greater percentage of the lactate clearance). Beyond the liver and skeletal muscle, in each of these scenarios, the heart and / or kidneys may also be responsible for a certain percentage of the lactate clearance.

[0152] Thus, to distinguish hepatic lactate clearance from muscle lactate clearance, cardiac lactate clearance, and / or renal lactate clearance, the decision support system 100 may analyze the user's non-analyte sensor data patterns to identify periods of both physical exertion and inactivity and determine hepatic lactate clearance. In particular, using a mapping of the non-analyte sensor data patterns to lactate clearance breakdown (e.g., the percentage of lactate clearance performed by the liver, skeletal muscle, heart, and / or kidneys), the decision support engine 114 may be able to better separate hepatic lactate clearance from the first lactate clearance value calculated in block 512.

[0153] Such a mapping may be predefined based on population data and / or the user's own data. The mapping may provide a mapping between non-analyte sensor data patterns, including accelerometer data patterns, respiration sensor data patterns, and / or heart rate monitor data patterns, and lactate clearance breakdown for each combination of these patterns.

[0154] For example, accelerometer data, heart rate data, and / or respiration data patterns showing elevated values ​​may indicate periods of increased physical exertion by the user. For periods of increased physical exertion, different accelerometer data, heart rate data, and / or respiration data patterns may be mapped to percentages of lactate clearance (or lactate production) performed by the liver, heart, kidneys, and skeletal muscle, respectively. Different activity types and / or different intensity levels may result in percentage variations for different non-analyte sensor data patterns.

[0155] Alternatively, accelerometer data, heart rate data, and / or respiration data patterns showing lower values ​​may indicate periods of minimal physical exertion or periods of sedentary activity by the user. For periods of minimal physical exertion, different accelerometer data, heart rate data, and / or respiration data patterns may be mapped to percentages of lactate clearance performed by each of the liver, heart, kidneys, and skeletal muscle. Different activity types and / or different levels of low physical exertion may result in percentage variations for different non-analyte sensor data patterns.

[0156] In certain embodiments, urinary lactate levels may be used as an input to the decision support engine 114 to inform such mapping to more accurately predict lactate cleared by the kidneys. Both sedentary and activity data pattern mapping may be used to isolate lactate hepatic clearance by a user. In particular, based on different patterns of non-analyte sensor data, the decision support system 100 may determine whether a user is in an active or sedentary state during periods of increased lactate clearance.

[0157] At block 512, the decision support engine 114 determines whether the at least one lactate increase period is attributable to the user's physical exertion. In some cases, the decision support engine 114 may make this determination based on non-analyte sensor data patterns (including, for example, accelerometer data patterns, respiration sensor data patterns, and / or heart rate monitor data patterns) that indicate whether the user is active or inactive. In some other cases, the decision support engine 114 may make this determination based on input provided by the user through the application 106 (e.g., exercise logging, lactate consumption logging, lactate infusion logging, etc.). If, at block 512, the decision support engine 114 determines that the at least one lactate increase period is not attributable to the user's physical exertion, the decision support engine 114 determines that the user's increased lactate concentration during the identified period is attributable, at least in part, to lactate consumption or lactate infusion.

[0158] In some cases, the user may consume lactate of their own free will, and in other cases, the user may be instructed to consume lactate to increase the user's lactate level for measurement. Additionally, pyruvate, pyruvic acid, and / or other substances may be consumed to produce lactate upon their decomposition. As mentioned, lactate for consumption may include any natural or engineered food or beverage that contains lactate or other molecules designed to stimulate the production, metabolism, clearance, consumption, degradation, or release of lactate, and / or metabolites that replace and / or are derived from lactate in a measurable manner. Additionally, synthetic lactate molecules or molecular mimetics with enhanced diagnostic detection elements (e.g., using radioactive or non-natural isotopes, enantiomers, quantum dot-labeled probes, and / or other molecular discrimination techniques) can be used to distinguish between the clearance of synthetic lactate versus naturally produced lactate by directly or indirectly measuring lactate and / or degradation products, through inference of another analyte.

[0159] In some other cases, the user may be infused with lactate, consume lactate, or consume a lactate-producing meal (e.g., fructose) for measurement by the continuous analyte monitoring system 104 to better determine the user's lactate clearance rate. For example, in some cases, lactate infusion may be used as a control scenario to determine the user's lactate clearance. This method may be an artificial method that challenges the user's organs by putting more lactate into the body in order to have direct knowledge of how much lactate is being input for clearance (e.g., because the operator of the lactate infusion pump is controlling the infusion and the pump). For example, this process may involve infusing lactate into the user's body at a rate of 10 mL / hr, 20 mL / hr, or 30 mL / hr until the lactate reaches a control amount, and allowing the user's organs to remove the infused lactate. In certain embodiments, instead of lactate infusion, lactate may be consumed. Oral lactate consumption undergoes first-pass hepatic metabolism, and therefore the peak lactate level after consumption of a standardized drink or meal can be a method to isolate the liver and evaluate its health, unless the subject is exercising during lactate consumption.If lactate metabolism is impaired in the liver, patients with liver disease may show a higher lactate increase rate, a longer duration of peak lactate levels, slower lactate clearance, and / or higher than expected values ​​after consumption of a lactate meal / drink.

[0160] Continuing with the example provided above, based on either user input or analysis of one or more patterns of data from the one or more non-analyte sensors, at block 512 the decision support engine 114 determines that during an identified first period of increased lactate levels (e.g., between 9:00 AM and 10:00 AM), a maximum lactate level of 8 mmol / L was achieved over a period of physical exertion by the user. Additionally, the decision support engine 114 determines that during an identified second period of increased lactate levels of the user (e.g., between 1:00 PM and 1:30 PM), a maximum lactate level of 5 mmol / L was achieved over a period of inactivity (or no physical exertion) by the user.

[0161] In block 512, the decision support engine 114 determines that the user's second period of increased lactate levels (e.g., between 1:00 p.m. and 1:30 p.m.) is not due to physical exertion, so in block 514, the decision support engine 114 determines whether to assume that lactate in the user's body is cleared only by the liver.

[0162] In particular, in certain embodiments, the decision support engine 114 may be configured to assume that if the user is determined to be sedentary (e.g., no physical activity), the lactate clearance rate calculated in block 504 is primarily indicative of hepatic lactate clearance. In other words, the decision support engine 114 may assume that during sedentary behavior, other organs are not performing significant amounts of clearance, and thus no correction is needed to separate hepatic lactate clearance from the lactate clearance rate calculated in block 504. For example, the decision support engine 114 may assume that when the user consumes a lactate drink, the user's muscles may not be actively producing lactate, and thus no correction is needed to separate hepatic lactate clearance from the lactate clearance rate calculated in block 504. Thus, in block 516, the user's hepatic lactate clearance is determined to be the first lactate clearance rate (e.g., calculated in block 410). As described in more detail below, the decision support engine 114 may use this first lactate clearance as a metric to predict the presence and / or severity of liver disease in the user.

[0163] In certain other embodiments, the decision support engine 114 may be configured to conclude that if the user is determined to be sedentary (e.g., physically active), the lactate clearance rate calculated in block 410 is indicative of lactate clearance performed by the liver as well as other organs. In other words, the user is determined to be inactive during periods of high lactate concentrations, but the sedentary lactate clearance rate may not represent 100% hepatic lactate clearance.

[0164] Thus, in block 518, the decision support engine 114 compares the first lactate clearance rate calculated (e.g., in block 410) for at least one lactate increase period to one or more lactate clearance rates calculated for one or more periods of sedentary behavior by the user, each of the one or more other lactate clearance rates representing an aggregate of lactate clearance by at least one of the liver, kidneys, muscle, and / or heart. In particular, the decision support engine 114 may compare the user's non-analyte sensor data pattern to a mapping of non-analyte sensor data patterns (e.g., indicative of sedentary behavior) to a predefined lactate clearance rate breakdown (e.g., percentage of lactate clearance performed by the liver, skeletal muscle, heart, and / or kidneys). Based on the comparison, the decision support engine 114 may identify a non-analyte sensor data pattern within the mapping that most closely resembles the user's current non-analyte sensor data pattern (e.g., indicative of sedentary activity). The identified non-analyte sensor data patterns map to a predetermined lactate clearance rate profile that the decision support system 100 identifies as the user's lactate clearance rate profile.

[0165] As an illustrative example, the non-analyte data collected about the user may include accelerometer data and respiration data if the user is using an accelerometer and a respiration monitor. The accelerometer data collected about the user may represent a first pattern X, and the respiration data collected about the user may represent a second pattern Y. The decision support engine 114 may compare these two patterns to a mapping of other non-analyte sensor data patterns. The first non-analyte sensor data pattern may include accelerometer data pattern A and respiration data pattern B. For this first non-analyte sensor data pattern, it may be previously determined that the liver removes 70% of the lactate in the body, and the kidneys and muscles remove the remaining 30%. The second non-analyte sensor data pattern may include accelerometer data pattern X and respiration data pattern Y. For this second non-analyte sensor data pattern, it may be previously determined that the liver removes 60% and the kidneys and muscles remove the remaining 40%. During the comparison, the decision support engine 114 may determine that the accelerometer data pattern X and the respiration data pattern Y most closely resemble the second non-analyte sensor data pattern. Thus, the decision support engine 114 may determine that 60% of the removed lactate is removed by the liver based on a predetermined lactate clearance rate breakdown for the second non-analyte sensor data pattern.

[0166] In block 522, the decision support engine 114 determines a second lactate clearance rate indicative of hepatic lactate clearance based at least in part on the comparison performed in block 518. For example, if a similar non-analyte data pattern is present in the mapping, the decision support engine 114 may determine, based on the user's non-analyte data, that the user's liver is likely removing only 70% of the lactate in the user's body. Thus, the decision support engine 114 may adjust the lactate clearance rate calculated in block 410 based at least in part on the determination that the liver is likely contributing only 70% of the calculated clearance. Thus, in block 524, the user's hepatic lactate clearance rate is determined to be the second lactate clearance rate (e.g., calculated in block 522). As described in more detail below, the decision support engine 114 may use this second lactate clearance as a metric to predict the presence and / or severity of liver disease in the user.

[0167] Alternatively, returning to block 512, if it is determined that at least one period of increased lactate is attributable to physical exertion, the decision support engine 114 then determines that the user's increased concentrations during this identified period are due, at least in part, to increased activity. For example, in block 512, because the decision support engine 114 determines that the first period of increased lactate levels (e.g., between 9:00 AM and 10:00 AM) is attributable to physical exertion, the decision support engine 114 may assume that the user engaged in some physical activity or exercise between the hours of 9:00 AM and 10:00 AM.

[0168] In some cases, the user may exercise of their own volition, while in other cases the user may be instructed to engage in some form of exercise or physical exertion (also referred to as exercise-induced lactate analysis). For example, one concept for measuring lactate levels is to have the user exercise at an intensity such that the user's lactate level increases to a certain level, for example between 4-10 mmol / L, or reaches the user's lactate threshold. Once this level is achieved, exercise may be stopped and lactate clearance may be measured. The lactate level may be held at a particular value, such as 5 mmol / L, for a period of time (e.g., 5-10 minutes) before stopping the exercise.

[0169] Continuing with the example provided above, in block 512, the decision support engine 114 determines that a maximum lactate level of 8 mmol / L for the user was achieved during a period of physical exertion, so in block 520, the decision support engine 114 compares the non-analyte sensor data pattern for the user (e.g., indicative of physical exertion) to a mapping of the non-analyte sensor data pattern to a predefined lactate clearance rate breakdown (e.g., percentage of lactate clearance performed by the liver, skeletal muscle, heart, and / or kidneys). The decision support engine 114 may perform such a comparison to identify the non-analyte sensor data pattern that most closely resembles or is associated with the user's current non-analyte sensor data pattern (e.g., indicative of physical exertion). The identified non-analyte sensor data pattern is mapped to a predefined lactate clearance rate breakdown based on what the decision support engine 114 identifies as the user's lactate clearance rate being performed by the user given the user's current activity level.

[0170] In block 522, the decision support engine 114 determines a second lactate clearance rate indicative of hepatic lactate clearance based at least in part on the comparison in block 520. For example, if there is a similar non-analyte data pattern, the decision support engine 114 may determine based on the user's non-analyte data that the user's liver is likely removing only 50% of the lactate in the user's body. Thus, the decision support engine 114 may adjust the lactate clearance rate calculated in block 504 based at least in part on the determination that the liver is likely contributing only 50% of the calculated clearance. Thus, in block 524, the user's hepatic lactate clearance rate is determined to be the second lactate clearance rate (e.g., calculated in block 522). As described in more detail below, the decision support engine 114 may use this second lactate clearance as a metric to predict the presence and / or severity of liver disease in the user.

[0171] In certain embodiments where two or more periods of increased lactate levels are identified in block 502 and analyzed to determine a plurality of hepatic lactate clearance rates, each calculated (and in some cases corrected) hepatic lactate clearance rate may be independently used as an input to diagnose and / or stage a user's liver disease. In certain other embodiments, an average hepatic lactate clearance rate may be determined based on one or more of the calculated hepatic lactate clearance rates, and the average calculated hepatic lactate clearance rate may be independently used as an input to diagnose and / or stage a user's liver disease.

[0172] 4, the method 400 continues at block 414 by the decision support system 100 generating a disease prediction using the analyte data associated with one or more analytes and at least one lactate clearance rate (e.g., determined according to workflow 500 of FIG. 5 and in some cases corrected). Block 414 may, in certain embodiments, be performed by the decision support engine 114 illustrated in FIG.

[0173] Different methods for generating disease predictions may be used by the decision support engine 114. In particular, in certain embodiments, the decision support engine 114 may use rule-based models to provide real-time decision support for liver disease diagnosis and staging. Rule-based models involve using a set of rules to manipulate and / or analyze data. These rules may also be referred to as "If statements" because they tend to follow the lines of "If X happens then do Y." In particular, the decision support engine 114 may apply rule statements (e.g., if, then statements) to assess the presence and severity of liver disease in the user, perform liver disease risk stratification for the user, and / or identify risks (e.g., mortality risk, liver cancer risk, etc.) associated with the user's current liver disease diagnosis.

[0174] For example, a first rule may be "If the patient's hepatic lactate clearance rate falls between X and Y, then the patient has liver disease stage 1 of a particular scoring system (or corresponds to a first METAVIR score)," while a second rule may be "If the hepatic lactate clearance falls between Y and Z, then the patient has liver disease stage 2 of a particular scoring system (or corresponds to a second METAVIR score)." The determined hepatic lactate clearance (e.g., determined in block 408) may be applied against these predefined rules to stage the liver disease.

[0175] Such rules may be defined and maintained by the decision support engine 114 in a reference library. For example, the reference library may maintain ranges of hepatic lactate clearance rates that may be mapped to liver disease stages. In certain embodiments, such rules may be determined based on the training server system 140 analyzing historical patient records from the historical record database 112.

[0176] 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. Increasing granularity may provide more accurate output. As an example, including age ranges in the rules-based approach used by the decision support engine 114 may help inform differences in lactate clearance rates so that liver disease prediction, staging, diagnosis, etc. by the decision support engine 114 may be more accurate. For example, the average liver lactate clearance rate of teenagers (e.g., 13-17 years old) may be different from the average lactate clearance rate of middle-aged adults (e.g., 30-50 years old), and therefore age may be an important factor to analyze in the rules-based approach to better predict and stage liver disease in users.

[0177] In certain embodiments, as an alternative to using rule-based models, AI models such as machine learning models may be used to provide real-time decision support for diagnosing and staging liver disease. In certain embodiments, the decision support engine 114 may deploy one or more of these machine learning models to perform prediction of the user's glycemic events, diagnosis, staging, and risk stratification of liver disease in the user. Risk stratification may refer to the process of assigning health risk states to a user and using the user's assigned risk states to direct and improve care.

[0178] In particular, the decision support engine 114 may retrieve information from a user profile 118 associated with the user stored in the user database 110, characterize the information about the user 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's 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 machine learning, a feature is an individual measurable characteristic or feature useful for analysis. In certain embodiments, the features associated with the user may be used as inputs to one or more of the models to assess the presence and severity of liver disease in the user. In certain embodiments, the features associated with the user may be used as inputs to one or more of the models to risk stratify the user to identify whether the user is at high or low risk of developing liver disease. In certain embodiments, the features associated with the user may be used as inputs to one or more of the models to identify the risk (e.g., mortality risk, liver cancer risk, etc.) associated with the user's current liver disease diagnosis.

[0179] In certain embodiments, the features associated with the user may be used as inputs to one or more of the models to perform any combination of the functions described above. Details associated with how one or more machine learning models can be trained to provide real-time decision support for the diagnosis and staging of liver disease are further discussed in connection with FIG.

[0180] As mentioned, in certain embodiments, other analyte data, in addition to lactate, may be used by the decision support engine 114 to generate a glycemic event prediction for the user, in block 414. Analyte data including lactate and glucose data, lactate and ketone data, lactate and potassium data, or lactate, glucose, potassium, and ketone data (e.g., from measurements by the continuous analyte monitoring system 104) may be used as inputs to such machine learning models and / or rules-based models to predict the presence and severity of liver disease in the user.

[0181] The decision support engine 114 may use machine learning models and / or rule-based models to generate disease predictions based on continuous analysis of data (e.g., analyte data, and in some cases non-analyte data) about the user collected over various time periods. Analysis of data collected about the user over various time periods may provide insight into whether the user's health and / or disease is improving or worsening. For example, a user previously diagnosed with liver disease using the models discussed herein may continue to be monitored (e.g., collected continuously for the user) on an ongoing basis to determine whether the disease is worsening or improving, etc. As an example, a comparison of a user's lactate clearance rate, peak lactate level, baseline lactate level, and / or lactate production rate (e.g., after lactate consumption) over multiple months may indicate the user's disease progression.

[0182] In some cases, the method 400 continues at block 416 by the decision support engine 114 generating one or more recommendations for treatment based at least in part on the disease prediction generated at block 414. In particular, the decision support engine 114 makes a liver disease treatment decision or recommendation for the user. The treatment recommendation may include a recommendation of lifestyle modifications and / or one or more medications to prescribe, titrate, or avoid use by the user. The decision support engine 114 may output such recommendations for treatment to the user (e.g., through the application 106).

[0183] As an illustrative example, in some cases, the decision support engine 114 may determine that liver disease in a user is progressing and correlate such progression with medications previously prescribed for the user. For example, hepatic lactate clearance rate may be severely impaired in the presence of acute liver toxicity from certain medications. Thus, based on inputted medication consumption information for the user (in combination with other factors), the decision support engine 114 may determine that such progression of disease is due to one or more medications previously prescribed to the user. Thus, in certain embodiments, the decision support engine 114 may recommend to the user in block 416 that they stop taking the previously prescribed medication, and in some cases, recommend an alternative medication for consumption by the user. In certain other embodiments, the decision support engine 114 may recommend in block 416 that the user take a lower dosage of the previously prescribed medication. In certain embodiments, the decision support engine 114 may recommend a titration of the dosage of the previously prescribed medication to determine an ideal dosage for the user (e.g., while monitoring the user's liver health).

[0184] 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 6 describes in further detail a technique for training the machine learning models deployed by the decision support engine 114 for diagnosis, staging, and risk stratification of liver disease in a patient, e.g., a user, in accordance with certain embodiments of the present disclosure.

[0185] 6 is a flow diagram illustrating a method 600 for training a machine learning model to provide a prediction of a liver disease diagnosis according to certain embodiments of the present disclosure. In certain embodiments, the method 600 is used to train a model to assess the presence and / or severity of liver disease in a patient, for example, a user as illustrated in FIG.

[0186] The method 600 begins at block 602 with retrieving data from a medical history record database, such as the medical history record database 112 illustrated in Figure 1, by a training server system, such as the training server system 140 illustrated in Figure 1. As referred to herein, the medical history record database 112 may provide a repository of up-to-date and medical history information for users of a continuous analyte monitoring system and connected mobile health application, such as users of the continuous analyte monitoring system 104 and application 106 illustrated in Figure 1, as well as data for one or more patients who are not or were not previously users of the continuous analyte monitoring system 104 and / or application 106. In certain embodiments, the medical history record database 112 may include one or more data sets of historical patients who do not have liver disease or who have various stages of liver disease.

[0187] Retrieval of data from the historical record database 112 by the training server system 140, in block 602, may include retrieval of all or any subset of the information maintained by the historical record database 112. For example, if the historical record database 112 stores information about 100,000 patients (e.g., non-users and users of the continuous analyte monitoring system 104 and application 106), the data retrieved by the training server system 140 to train one or more machine learning models may include information about all 100,000 patients, or only a subset of the data for those patients, e.g., data associated with only 50,000 patients, or only data from the past 10 years.

[0188] As an illustrative example, integration with home-grown 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 the cloud-based repository.

[0189] As an illustrative example, in block 602, the training server system 140 may retrieve information about 100,000 patients with various histories of liver disease stored in the medical history record database 112 to train a model to predict the risk, presence, and / or severity of liver disease in a user. 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.

[0190] 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 contained 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 may be used as input features to train the ML model. For example, a patient record may include or be used to generate features related to the patient's age, the patient's sex, the patient's occupation, lactate clearance rate, area under the lactate curve, the average change in lactate clearance from a first timestamp to a subsequent timestamp (e.g., average delta), other lactate metrics described herein, the average change in liver disease diagnosis from a first timestamp to a subsequent timestamp (e.g., average delta), and / or any other data point in the patient record (e.g., inputs 128, metrics 130, etc.). The features used to train the machine learning model may vary in different embodiments.

[0191] In certain embodiments, each historical patient record retrieved from the historical record database 112 is further associated with a label indicating whether the patient was healthy or experienced some fluctuation in liver disease, the patient's previously determined liver disease diagnosis and / or stage of liver disease, previously assigned Child-Pugh score, MELD score, and / or METAVIR score, NAFLD score, NASH score, risk assessment, treatment recommendation, and similar metrics. What the record is labeled depends on what the model is trained to predict.

[0192] At block 604, the method 600 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. The model may be a new model initialized with random weights and parameters, or may be partially or fully pre-trained (e.g., based on a previous training round). Based on the input features, the model in training generates some output. In certain embodiments, the output may indicate whether the patient was healthy or experienced some variation in liver disease, a liver disease diagnosis and / or liver disease stage for the patient, a Child-Pugh score, a MELD score, a METAVIR score, a NAFLD score, a NASH score, a risk assessment, a treatment recommendation, or a similar output. It should be noted that the output may be in the form of a likelihood, a classification, and / or other types of output.

[0193] In certain embodiments, the training server system 140 compares this generated output to the actual labels associated with the corresponding historical patient records to calculate a loss based on the difference between the actual outcome and the generated outcome. This loss is then used to refine (e.g., via backpropagation) one or more internal weights and parameters of the model so that the model learns to more accurately predict the presence and / or severity of liver disease (or its recommended treatment).

[0194] 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.

[0195] At block 606, the training server system 140 deploys the trained model to make predictions associated with liver disease at run time. 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 assess the presence and / or severity of a user's liver disease in real time using the application 106, provide treatment recommendations, and / or make other types of predictions discussed above. 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.

[0196] Further, the same method for training illustrated in FIG. 6 using historical patient records may also be used to train a model using patient-specific records to create a more personalized model for making predictions associated with liver disease. For example, a model trained using historical patient records deployed for a particular user may be further retrained after deployment. For example, a model may be retrained after it is deployed for a particular patient to create a more personalized model for the patient. The more personalized model may be able to more accurately make liver disease-related predictions for a patient based on the patient's own data, including the patient's own inputs 128 and metrics 130 (as opposed to only historical patient record data).

[0197] 7 is a block diagram illustrating a computing device 700 configured to execute a decision support engine (e.g., the decision support engine 114) according to certain embodiments disclosed herein. Although depicted as a single physical device, in embodiments, the computing device 700 may be implemented using virtual devices and / or across several devices, such as in a cloud environment. As illustrated, the computing device 700 includes a processor 705, a memory 710, a storage 715, a network interface 725, and one or more I / O interfaces 720. In the illustrated embodiment, the processor 705 retrieves and executes programming instructions stored in the memory 710, as well as stores and retrieves application data present in the storage 715. The processor 705 generally represents a single CPU and / or GPU, multiple CPUs and / or GPUs, a single CPU and / or GPU with multiple processing cores, etc. The memory 710 is included to generally represent random access memory. Storage 715 may be any combination of disk drives, flash-based storage devices, and the like, and may include fixed and / or removable storage devices such as fixed disk drives, removable memory cards, cache, optical storage, network attached storage (NAS), or a storage area network (SAN).

[0198] In some embodiments, input and output (I / O) devices 735 (keyboard, monitor, etc.) may be connected via I / O interface 720. Additionally, via network interface 725, computing device 700 may be communicatively coupled to one or more other devices and components, such as user database 710. In particular embodiments, computing device 700 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 705, memory 710, storage 715, network interface 725, and I / O interface 720 are communicatively coupled by one or more interconnects 730. In particular embodiments, computing device 700 represents a mobile device 107 associated with a user. In particular embodiments, as discussed above, mobile device 107 may include a user's laptop, computer, smartphone, etc. In another embodiment, computing device 700 is a server running in a cloud environment.

[0199] In the illustrated embodiment, the storage 715 includes a user profile 118. The memory 710 includes a decision support engine 114, which itself includes a DAM 116. The decision support engine 114 is executed by the computing device 700 to perform operations 402-416 of the method 400 in FIG.

[0200] As described above, the continuous analyte monitoring system 104 described in connection with Figure 1 can be a multi-analyte sensor system that includes a multi-analyte sensor. Figures 8A-12 describe an exemplary multi-analyte sensor used to measure multiple analytes.

[0201] 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 to be limited to any special or customized meaning), and refer to, but are not limited to, an apparatus and / or system responsible for detecting a specific analyte or combination of analytes or converting 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 embodiment, the instrument includes a sensor coupled to a circuit arranged within a housing and configured to process a signal associated with an analyte concentration into information. In one embodiment, such an apparatus 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 conversion (detection) element.

[0202] As used herein, the terms "biosensor" and / or "sensor" are broad terms and are to be given their ordinary and customary meaning to one of ordinary skill in the art (and are not to be limited to any special or customized meaning), and refer to, but are not limited to, a portion of an analyte measuring device, analyte monitoring device, analyte sensing device, and / or multi-analyte sensing device responsible for detecting a particular analyte or combination of analytes or transducing a signal associated therewith. In one embodiment, a biosensor or sensor generally comprises a body and a working electrode, a reference electrode, and / or a counter electrode 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 embodiment, such biosensors and / or sensors are capable of providing a particular quantitative, semi-quantitative, qualitative, semi-qualitative analytical signal using a biological recognition element combined with a transduction (detection) element.

[0203] As used herein, the terms "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 to be limited to any special or customized meaning), and refer to, but are not limited to, a 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 embodiment, the sensing moiety, sensing membrane, and / or sensing mechanism generally comprises an electrode configured to provide a signal during an electrochemical reaction with one or more membranes covering an electrochemically reactive surface. In one embodiment, such a sensing moiety, sensing membrane, and / or sensing mechanism can provide specific quantitative, semi-quantitative, qualitative, semi-qualitative analytical information using a biological recognition element combined with a transduction (detection) element.

[0204] As used interchangeably herein, the phrases "biointerface membrane" and "biointerface layer" are broad terms and are to be given their ordinary and customary meaning to one of ordinary skill in the art (and are not to be 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 serves as a bioprotective interface between a host tissue and an implantable device. The terms "biointerface" and "bioprotective" are used interchangeably herein.

[0205] As used herein, the term "cofactor" is a broad term 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.

[0206] As used herein, the term "continuous" 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 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.

[0207] As used herein, the phrases "continuous analyte sensing" and "continuous multi-analyte sensing" are broad phrases that are to be given their ordinary and accustomed meaning to one of skill in the art (and are not to be limited to any special or customized meaning) and refer to, but are not limited to, monitoring of analyte concentrations continuously, intermittently, and / or intermittently (but periodically), 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 monitoring of the analyte concentration 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 monitoring of the analyte concentration is performed between about every 8 hours and about every 12, 16, 20, or 24 hours. In further embodiments, the monitoring of the analyte concentration is performed between about every day and about every 1.5, 2, 3, 4, 5, 6, or 7 days. In further embodiments, the monitoring of the analyte concentration is performed between about every week and about every 1.5, 2, 3 weeks or more.

[0208] As used herein, the term "coaxial" should be interpreted broadly to include sensor architectures having elements aligned along a shared axis about 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 about a core layer, such as a core electrode or core polymer wire.

[0209] As used herein, the term "coupled" 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, two or more system elements or components that are configured to be electrically, mechanically, thermally, operatively, chemically, or otherwise attached to at least one. 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 coupled," and "operably coupled" may 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, the components are part of the same structure and / or are integrated with one another, such as when they are covalently, electrostatically, mechanically, thermally, magnetically, ionically associated, or physically captured or absorbed (i.e., "directly coupled" as in the absence of intervening elements). In other examples, the 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.

[0210] As used herein, the term "discontinuous" 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 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.

[0211] As used herein, the term "distal" 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 to be limited to any special or customized meaning), and refers to, but is not limited to, an area that is spaced relatively far from a reference point such as an origin or attachment point.

[0212] As used herein, the term "domain" is a broad term and is to be given its ordinary and customary meaning to one 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, 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 a combination thereof.

[0213] 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 (H2O2) as a by-product. H2O2 reacts with the surface of the working electrode to release two protons (2H + ), 2 electrons (2e - ), and one oxygen molecule (O2), which produces an electronic current that is detected. At the counter electrode, a reducible species, e.g., O2, is reduced at the electrode surface to balance the current produced by the working electrode.

[0214] As used herein, the term "electrolysis" is a broad term and is to be given its ordinary and accustomed meaning to one of 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 "oxidation-reduction") of a compound, either directly or indirectly, by one or more enzymes, cofactors, or mediators.

[0215] As used herein, the terms "in-dwelling," "in-dwelling," "implanted," or "implantable" are broad terms and are to be given their ordinary and customary meaning to one 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 includes a sensor that is inserted or configured to be inserted subcutaneously (i.e., into the fat layer between the skin and muscle), intradermally (i.e., penetrating the stratum corneum and located in the epidermal or dermal layer of the skin), or percutaneously (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 "in-dwelling" also encompasses an object that is configured to be inserted subcutaneously, intradermally, or percutaneously, whether or not it is itself inserted.

[0216] As used herein, the terms "interfering substance" and "interfering species" are broad terms and are to be given their ordinary and customary meaning to one of skill in the art (and are not to be 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 to produce a signal that is not accurately representative of the analyte measurement. In one embodiment 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.

[0217] As used herein, the term "in vivo" is intended to be broad 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 encompasses, but is not limited to, portions of a device (e.g., a sensor) that are adapted for insertion into and / or presence within the body of a host.

[0218] 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), and encompasses, but is not limited to, a portion of a device (e.g., a sensor) that is adapted to reside and / or exist outside the host organism.

[0219] As used herein, the terms and phrases "mediator" and "redox mediator" are broad terms and phrases that are to be given their ordinary and customary meaning to one of skill in the art (and are not to be limited to any special or customized meaning) and refer to, but are not limited to, any chemical compound or collection of compounds 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 electrical 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 example, the mediator is a transition metal-coordinated organic molecule capable of reversible oxidation and reduction reactions. In other examples, the mediator can be an organic molecule or a metal capable of reversible oxidation and reduction reactions.

[0220] As used herein, the term "membrane" 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 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 blocking 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.

[0221] As used herein, the phrase "membrane system" is a broad phrase and is to be given its ordinary and customary meaning to one of skill in the art (and is not to be 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, typically composed of materials several microns thick or greater, and that is permeable to oxygen and, optionally, permeable to, 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.

[0222] As used herein, the term "planar" should be broadly construed to describe a sensor architecture having a substrate including at least a first surface and an opposing second surface, e.g., comprising 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 coupled, electrically or otherwise. In one embodiment, the planar surface includes one or more edges separating the opposing surfaces.

[0223] As used herein, the term "proximal" is a broad term and is 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 spatial relationship between various elements compared to a particular reference point. For example, some embodiments of the device include a membrane system having a biointerface 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 biointerface layer, then the enzyme domain is more proximal to the sensor than the biointerface layer.

[0224] As used herein, the terms "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 to be limited to any special or customized meaning), and refer to, but are not limited to, a 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 embodiment, the sensing moiety, sensing membrane, and / or sensing mechanism generally comprises an electrode configured to provide a signal during an electrochemical reaction with one or more membranes covering an electrochemically reactive surface. In one embodiment, such a sensing moiety, sensing membrane, and / or sensing mechanism can provide specific quantitative, semi-quantitative, qualitative, semi-qualitative analytical information using a biological recognition element combined with a transduction (detection) element.

[0225] 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 components thereof with the analyte measuring device, biosensor, sensor, sensing region, sensing moiety, or sensing mechanism results in the transduction of a signal that allows for a qualitative, semi-qualitative, quantitative, or semi-qualitative determination of an analyte level, e.g., glucose, ketones, lactate, potassium, etc., in the biological sample.

[0226] 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) that form an electrochemically reactive surface at one location on the body and 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 including 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).

[0227] In another example, the sensing region can include one or more periplasmic binding proteins (PBPs), including mutants or fusion proteins thereof, or aptamers with one or more analyte binding regions, each region capable of specifically and reversibly binding at least one analyte. The alteration of the aptamer or the mutation of the PBP can contribute to or change one or more binding constants, long-term stability of the protein, including thermal stability, to bind the protein to a special encapsulation matrix, membrane or polymer, or to attach detectable reporter groups or "labels" 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 changes in the binding region include, but are not limited to, hydrophobic / hydrophilic environmental changes, three-dimensional conformational changes, changes in the orientation of amino / nucleic acid side chains in the binding region of the protein, and redox state of the binding region. Such changes to the binding region provide transduction of a detectable signal corresponding to one or more analytes present in the biological fluid.

[0228] In one embodiment, the sensing region determines selectivity between one or more analytes such that only those analytes that must be measured result in (transduce) 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.

[0229] As used herein, the term "sensitivity" is a broad term and is to be given its ordinary and customary meaning to one 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, 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 mg / dL of analyte.

[0230] 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.

[0231] 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 special or customized meanings), 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, the sensing region converts the recognition of an analyte into a semi-quantitative or quantitative signal.

[0232] The phrase "transducing element" as used herein is a broad term and is 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, an analyte recognition moiety that can directly or indirectly facilitate detectable signal transmission corresponding to the presence and / or concentration of a recognized analyte. In one embodiment, 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 skin of a host 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's tissue. The 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.

[0233] Membrane Systems The membrane systems disclosed herein are suitable for use in implantable devices that contact biological fluids. For example, the membrane systems can be utilized with implantable devices such as devices for monitoring and determining analyte levels in biological fluids, e.g., devices for monitoring glucose levels for individuals with diabetes. In some embodiments, the analyte measuring device is a continuous device. The analyte measuring 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.

[0234] 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 by reference herein in their entireties for their teachings of membrane systems.

[0235] Generally, the membrane system includes multiple domains, such as an electrode domain, an interference domain, an enzyme domain, a resistance domain, and a biointerface domain. The membrane system may 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 may be applied following deposition of the membrane material, such as drying, annealing, and curing (e.g., UV curing, heat curing, moisture curing, radiation curing, etc.) to improve certain 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 the cured film cast from the coating formulation by standard coating techniques.

[0236] In certain examples, the biointerface / drug release layer coating is formed from a biointerface polymer, the biointerface polymer comprising one or more membrane domains including 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 non-ionic hydrophilic polyethylene oxide segments incorporated into the polymer, the polyurethaneurea polymer is dissolved in an organic solvent or non-organic solvent system according to a given 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 the polymerization is completed. The solvent is selected from those with lower boiling points to facilitate drying and lower toxicity for implant applications. Examples of these solvents include aliphatic ketones, esters, ethers, alcohols, hydrocarbons, and the like. Depending on the final thickness of the biointerface / drug release layer and the solution viscosity (related to the percent of polymer solids), the coating may 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 carboxylbetaine groups incorporated into the polymer and non-ionic hydrophilic polyethylene oxide segments, and the polyurethaneurea polymer is dissolved in an organic solvent or non-organic solvent system in the coating formulation, crosslinked with a carbodiimide (e.g., 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC)), and cured at moderate temperatures of about 50°C.

[0237] 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 being dissolved in an organic or non-organic solvent system according to a given 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 the polymerization is completed. Solvents are 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, and the like. Depending on the final thickness of the biointerface / drug-release layer and the solution viscosity (related to the percentage of 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 embodiment, the biointerface polymer is formed from a polyurethaneurea having unsaturated hydrocarbon groups and sulfobetaine groups incorporated into the polymer, and non-ionic hydrophilic polyethylene oxide segments, where the polyurethaneurea polymer is dissolved in an organic solvent or non-organic solvent system in a coating formulation and crosslinked in the presence of an initiator by heat or irradiation including UV, LED light, electron beam, etc., and cured at moderate temperatures of about 50° C. Examples of unsaturated hydrocarbons include allyl groups, vinyl groups, acrylates, methacrylates, alkenes, alkynes, etc.

[0238] 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 the 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) with an adjustable 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 effective enzyme catalysis, and "effective" enzyme catalysis allows the analyte sensor to continuously monitor one or more analytes over a period of about 5 days to about 15 days or more.

[0239] membrane manufacturing Polymers can be processed by solution-based techniques, such as, for example, spraying, dipping, casting, electrospinning, vapor deposition, spin-coating, coating, etc. 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 the 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.

[0240] 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.

[0241] 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 be formed between two polymers as a result of high temperatures. In some embodiments, photocrosslinking is performed to form covalent bonds between the polycation layer and the polyanion layer. One major advantage of photocrosslinking is that it offers the possibility of patterning. In certain embodiments, patterning using photocrosslinking has been performed to modify the film structure and thus adjust the wetting properties of the membrane and membrane system, as discussed herein.

[0242] Polymers having domains or segments functionalized to allow crosslinking can be made 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 crosslinkers having multiple nucleophilic groups (e.g., hydroxyl, amine, urea, urethane, or thiol groups). In further examples, polyurethaneurea polymers having aromatic or aliphatic segments with nucleophilic functional groups can be crosslinked with crosslinkers having multiple electrophilic groups. Furthermore, polyurethaneurea polymers having hydrophilic segments with nucleophilic or electrophilic functional groups can be crosslinked with crosslinkers having multiple electrophilic or nucleophilic groups. Unsaturated functional groups on the polyurethaneurea can also be used for crosslinking by reacting with multivalent free radical agents. 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 (PEGDE), 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.

[0243] The polymers disclosed herein can be formulated into a mixture that can be stretched into a film or applied to a surface using methods such as self-assembling monolayer (SAM), spraying, painting, dip coating, vapor deposition, molding, 3D printing, lithography techniques (e.g., photolithography), micro- and nano-pipetting printing techniques, silk screen printing, and the like. 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.

[0244] In some circumstances, 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 impeding or blocking it. In another aspect, to extend the life of the sensor, one embodiment uses a material that promotes vascularized tissue ingrowth, for example, in a porous biointerface membrane. For example, tissue ingrowth into the porous biointerface material surrounding the sensor can promote sensor function over an extended period 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 by the use of a porous bioprotective material that surrounds the sensor and promotes ingrowth of tissue and microvasculature over time.

[0245] Thus, the sensor as 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 in the long term (e.g., after tissue ingrowth).

[0246] Thus, a sensor as discussed in the examples herein may include a drug-releasing membrane that functions at least partially as or in combination with a biointerface membrane. The drug-releasing membrane may 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 and the useful life of the sensor is extended. Other suitable drug release films of the present disclosure include silicone polymers, polytetrafluoroethylene, expanded polytetrafluoroethylene, ethylene tetrafluoroethylene copolymers, polyolefins, polyesters, polycarbonates, biostable polytetrafluoroethylene, polyurethane homopolymers, copolymers, terpolymers, polypropylene (PP), polyvinyl chloride (PVC), polyvinylidene fluoride (PVDF), polyvinyl alcohol (PVA), ethylene vinyl acetate (EVA), polybutylene terephthalate (PBT), polymethylmethacrylate (PMMA), polyether ether ketone ether (polyether ether ketone ether), ... polyvinyl chloride (PVDF), polyvinyl alcohol (PVA), polyvinyl chloride (PVDF), polyvinyl alcohol (PVA), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl alcohol (PVA), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyvinyl chloride (PVDF), polyketone, 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 (including, for example, 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 (PVC) and copolymers and blends thereof, 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, hydroxyapeptide and copolymers and blends thereof.

[0247] Exemplary Multi-Analyte Sensor Membrane Configurations Continuous multi-analyte sensors are provided having a variety of membrane configurations suitable for facilitating simultaneous, intermittent, and / or sequential signal transduction corresponding to analyte concentrations. In one embodiment, such sensors may be constructed using signal transducers that include one or more transducing elements ("TL"). Such continuous multi-analyte sensors may employ a variety of transduction means, such as amperometric, voltammetric, potentiometric, and impedance measurement methods, among other techniques.

[0248] In one embodiment, the transduction element includes 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 elements include enzymes, ionophores, RNA, DNA, aptamers, binding proteins, and are used interchangeably.

[0249] In one embodiment, the transducer element is present in one or more membranes, layers, or domains formed over the sensing region. In one embodiment, such sensors can be constructed using one or more enzyme domains, e.g., membrane domains that include enzyme domains, also referred to as EZ layers ("EZLs"), each of which can include one or more enzymes. References below to an "enzyme layer" are intended to include all or a portion of the enzyme domains, any of which can 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.

[0250] 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 may 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 that include recombinant enzymes, immobilized enzymes, mediator-wired enzymes, dimerized enzymes, and / or fusion enzymes.

[0251] A 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 the adenine nucleobase, and the other contains nicotinamide. NAD exists in two forms, e.g., oxidized (NAD(P)+) and reduced (NAD(P)H) (H=hydrogen). The reaction between NAD+ and NADH is reversible, and thus the coenzyme can be produced by NAD(P) without being consumed essentially. + / and NAD(P)H forms can be cycled continuously between them.

[0252] In one embodiment, one or more enzyme domains in a 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 embodiment, one or more enzyme domains in a 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.

[0253] In one embodiment, NAD, NADH, NAD+, NAD(P)+, ATP, flavin adenine dinucleotide (FAD), magnesium (Mg++), pyrroloquinoline quinone (PQQ), and functionalized derivatives thereof may be used in combination with one or more enzymes in a continuous multi-analyte sensor device. In one embodiment, NAD, NADH, NAD+, NAD(P)+, ATP, flavin adenine dinucleotide (FAD), magnesium (Mg++), pyrroloquinoline quinone (PQQ), and functionalized derivatives thereof 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 functionalized derivatives thereof are dispersed or distributed in one or more membranes or domains of the sensing region.

[0254] In one aspect of the disclosure, continuous sensing of one or more or two or more analytes using NAD+-dependent enzymes is provided in one or more membranes or domains of the sensing region. In one embodiment, the membrane or domain provides a mechanism for the retention and stable recycling of NAD+, and for converting NADH oxidation or NAD+ reduction into an amperometrically measurable current. In one embodiment 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 sequential on-body sensing of multiple analytes that utilize FAD-dependent dehydrogenases such as fatty acids (acyl-CoA dehydrogenase).

[0255] Exemplary configurations of one or more membranes or portions thereof are provided that are in an arrangement to provide for the retention and recycling of NAD+. Thus, the electrode surface of the conductive wire (coaxial) or the planar conductive surface is coated with at least one layer containing at least one enzyme, as depicted in FIG. 8A. Referring to FIG. 8B, one or more optional layers may be located 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 undesirable species present, or one or more electrodes (not shown) may be used to aid in wetting, system equilibration, and / or start-up. As shown in FIG. 8A and FIG. 8B, one or more membranes provide a NAD+ reservoir domain that provides a reservoir for NAD+. In one embodiment, one or more interferent blocking membranes are used and a potentiostat is utilized to measure H2O2 production or O2 consumption of an enzyme such as or similar to NADH oxidase, and the NAD+ reservoir and enzyme domain locations can be switched to facilitate better consumption and slower outward diffusion of excess NAD+. Exemplary sensor configurations can be found in U.S. Provisional Patent Application No. 63 / 321,340, entitled "CONTINUOUS ANALYTE MONITORING SENSOR SYSTEMS AND METHODS OF USING THE SAME," filed March 18, 2022, which is incorporated herein by reference in its entirety.

[0256] 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-phenanthroline-dione or osmium(bpy)2Cl, polymers containing covalently bound organic mediators or organometallic coordination mediator polymers, such as polyvinylimidizole-Os(bpy)2Cl, or polyvinylpyridine-organometallic coordination mediators (including ruthenium-phenanthrolinedione), are used. Other mediators may be used, as discussed further below.

[0257] In humans, serum levels of beta-hydroxybutyrate (BHB) are usually in the low micromolar range, but can rise to about 6-8 mM. Serum levels of BHB can reach 1-2 mM after vigorous exercise, or consistent levels above 2 mM are achieved by a ketogenic diet that contains very little carbohydrate. Although other ketones, such as acetoacetate and acetone, are present in serum, most of the dynamic range in ketone levels is in the form of BHB. Thus, monitoring, e.g., continuous monitoring, of BHB is useful to provide health information to users or health care providers.

[0258] Another example of a continuous ketone analyte detection configuration using an electrode-associated mediator-coupled diaphorase / NAD+ / dehydrogenase is depicted below.

[0259] [ka]

[0260] In one example, diaphorase is electrically coupled to an electrode having an organometallic coordination mediator polymer. In another example, diaphorase is covalently coupled to an electrode having an organometallic coordination mediator polymer. 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 coupled to a polymer and is 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. In one embodiment, 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+ in the enzyme domain. For example, dextran-NAD.

[0261] 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.

[0262] In one embodiment, a ketone sensing arrangement suitable for combination with another analyte sensing arrangement is provided. Thus, an EZL layer about 1-20 μm thick is prepared by providing an EZL solution composition in 10 mM HEPES in water having about 20 μL 500 mg / mL HBDH, about 20 μL [500 mg / mL NAD(P)H, about 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) to a substrate such as a working electrode, such that after drying, it provides 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 that 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.

[0263] The enzyme domain is contacted with a resistance domain, also referred to as a resistance layer ("RL"). In one embodiment, the RL comprises about 55-100% PVP and about 0.1-45% PEG-DGE. In another embodiment, the RL comprises about 75-100% PVP and about 0.3-25% PEG-DGE. In yet another embodiment, the RL comprises about 85-100% PVP and about 0.5-15% PEG-DGE. In yet another embodiment, the RL comprises essentially 100% PVP.

[0264] The exemplary continuous ketone sensor depicted in FIG. 8A and FIG. 8B that includes a NAD(P)H reservoir domain is configured such that NAD(P)H is non-limiting in any of the enzyme domains of the sensing region. In one embodiment, the loading of NAD(P)H in the NAD(P)H reservoir domain is greater than about 20%, 30%, 40% or 50% w / w. One or more membranes or portions of one or more membrane domains (hereinafter also referred to as "membranes") may also contain polymers or protein binders such as zwitterionic polyurethane and / or albumin. Alternatively, in addition to NAD(P)H, the membrane may contain one or more analyte-specific enzymes (e.g., HBDH, glycerol dehydrogenase, etc.), so that, optionally, the NAD(P)H reservoir membrane also provides catalytic function. In one example, NAD(P)H is dispersed or distributed within or along with the polymer (or protein) and may be crosslinked to an extent that still allows for proper enzyme / cofactor function and / or reduced NAD(P)H flux within the domain.

[0265] In one embodiment, NADH oxidase enzyme is used alone or in combination with superoxide dismutase (SOD) in one or more membranes of the sensing region. In one embodiment, an amount of superoxide dismutase (SOD) is used that can scavenge a portion or a majority of one or more free radicals generated by NADH oxidase. In one embodiment, NADH oxidase enzyme is used alone or in combination with superoxide dismutase (SOD) in combination with NAD(P)H and / or a functionalized polymer having NAD(P)H immobilized on the polymer from the C6-terminal amine in one or more membranes of the sensing region.

[0266] In one embodiment, NAD(P)H is immobilized to an extent that maintains NAD(P)H catalytic function. In one embodiment, dimerized NAD(P)H is used to capture NAD(P)H in one or more membranes by crosslinking their respective C6-terminal amines with a suitable amine-reactive crosslinker, such as glutaraldehyde or PEG-DGE.

[0267] The above-described continuous ketone sensor configurations may be adapted for other analytes or used in combination with other sensor configurations. For example, analyte-dehydrogenase enzyme combinations may be used in any of the membranes of sensing regions including glycerol (glycerol dehydrogenase), cortisol (11β-hydroxysteroid dehydrogenase), glucose (glucose dehydrogenase), alcohol (alcohol dehydrogenase), aldehydes (aldehyde dehydrogenase), and lactate (lactate dehydrogenase).

[0268] In one embodiment, a semi-permeable membrane is used in or adjacent to the sensing region, or adjacent to one or more membranes of the sensing region, to attenuate the flux of at least one analyte or species. In one embodiment, the semi-permeable membrane attenuates the flux of at least one analyte or species to provide a linear response from the transduced signal. In another embodiment, the semi-permeable membrane prevents or eliminates the flux of NAD(P)H from the sensing region or any membrane or domain. In one embodiment, the semi-permeable membrane can be an ion-selective membrane selective for an ionic analyte of interest, such as ammonium ion.

[0269] In another example, a continuous multi-analyte sensor configuration was prepared that includes one or more enzymes and / or at least one cofactor. FIG. 1C depicts this exemplary configuration of an enzyme domain 850 that includes an enzyme with an amount of cofactor located adjacent to at least a portion of a working electrode ("WE") surface, where the WE includes an electrochemically reactive surface. In one example, a second membrane 851 that includes an amount of cofactor is located adjacent to the first enzyme domain. The amount of cofactor in the second membrane can provide an excess amount for the enzyme, for example, to extend sensor life. One or more resistance domains 852 ("RL") are located adjacent to the second membrane (or can be between the membranes). The RL can be configured to block diffusion of the cofactor from the second membrane. Electron transfer from the cofactor to the WE transduce a signal that corresponds directly or indirectly to the analyte concentration.

[0270] 8D depicts an alternative enzyme domain configuration that includes a first membrane 851 with an amount of cofactor located more proximally for at least a portion of the WE surface. An enzyme domain 850, including an amount of enzyme, is located adjacent to the first membrane.

[0271] In the membrane configurations depicted in Figures 8C and 8D, production of an electrochemically active species in the enzyme domain diffuses to the WE surface and transduces a signal that corresponds directly or indirectly to an analyte concentration. In some embodiments, the electrochemically active species includes hydrogen peroxide. In sensor configurations that include a cofactor, the cofactor from the first layer can diffuse to the enzyme domain to extend sensor life, for example, by regenerating the cofactor. In other sensor configurations, a cofactor can be optionally included to improve performance attributes such as stability. For example, a continuous ketone sensor uses NAD(P)H and Mg +2The WE surface may include a divalent metal cation such as . One or more resistive domains RL may be located adjacent to the second membrane (or may be between the layers). The RL may be configured to block diffusion of cofactors and / or interferents from the second membrane from reaching the WE surface. Other configurations such as electrodes, resistors, biointerfaces, and drug-releasing membranes, layers, or domains may be used in the above configurations. In other examples, the continuous analyte sensor includes one or more cofactors that contribute to sensor performance.

[0272] FIG. 8E depicts another continuous multi-analyte membrane configuration in which a {beta}-hydroxybutyrate dehydrogenase BHBDH in a first enzyme domain 853 is located proximate to the working electrode WE, and a second enzyme domain 1854 including, for example, alcohol dehydrogenase (ADH) and NADH is located adjacent to the first enzyme domain. One or more resistor domains RL852 can be deployed adjacent to the second enzyme domain 854. In this configuration, the combined presence of alcohol and ketones in serum collectively serves to provide a transduced signal corresponding to at least one of the analyte concentrations, for example, ketones. Thus, as NADH present in the more distal second enzyme domain consumes alcohol present in the serum environment, NADH is oxidized to NAD(P)H and diffuses to the first membrane layer to provide BHBDH-catalyzed electron transfer of acetoacetate ketone and transduction of a detection signal corresponding to the concentration of ketone. In one example, an enzyme can be configured for reverse catalysis, creating a substrate that is used for catalysis of another enzyme, either in the same or a different layer or domain. Other configurations, such as electrodes, resistors, biointerfaces, and drug-releasing membranes, layers, or domains, can be used in the foregoing configurations. Thus, a first enzyme domain that is more distal to the WE than a second enzyme domain can be configured to generate a cofactor or other element for the second enzyme domain to act as a reactant (and / or reactant substrate) for detecting one or more target analytes.

[0273] Alcohol Sensor Configuration In one embodiment, a continuous alcohol (e.g., ethanol) sensor device configuration is provided, in which one or more enzyme domains including alcohol oxidase (AOX) are provided, where the presence and / or amount of alcohol is converted by the creation of hydrogen peroxide, either alone or in combination with oxygen consumption or another substrate-oxidase enzyme system, e.g., glucose-glucose oxidase, and the hydrogen peroxide and / or oxygen and / or glucose can be detected and / or measured qualitatively or quantitatively using amperometry.

[0274] In one embodiment, the sensing region for the enzyme substrate-oxidase enzyme configuration comprises one or more enzyme domains with one or more electrodes. In one embodiment, the sensing region for the enzyme substrate-oxidase enzyme configuration comprises one or more enzyme domains with or without one or more electrodes, and further comprises one or more interference barrier membranes (e.g., selectively permeable membranes, charge exclusion membranes) to attenuate diffusion of one or more interferents through the membrane to the working electrode. In one embodiment, the sensing region for the substrate-oxidase enzyme configuration comprises one or more enzyme domains with or without one or more electrodes, and further comprises one or more resistance domains with or without one or more interference barrier membranes to attenuate diffusion of one or more analytes or enzyme substrates. In one embodiment, the sensing region for the substrate-oxidase enzyme configuration comprises one or more enzyme domains with or without one or more electrodes, and further comprises one or more biointerface membranes and / or drug release membranes to attenuate diffusion of one or more analytes or enzyme substrates, and independently attenuate immune response of the host after insertion.

[0275] In one embodiment, one or more interference blocking films are deposited adjacent to the working electrode and / or electrode surface. In one embodiment, one or more interference blocking films are deposited directly adjacent to the working electrode and / or electrode surface. In one embodiment, one or more interference blocking films are deposited between another layer or film or domain adjacent to the working electrode or electrode surface to attenuate one or all analytes that diffuse through the sensing region, except for oxygen. Such films can be used not only to attenuate the alcohol itself, but also to attenuate other electrochemically active species or other analytes that may interfere by producing a signal if they diffuse to the working electrode.

[0276] In one embodiment, the working electrode used comprised platinum and the applied potential was about 0.5 volts.

[0277] In one embodiment, electrochemical sensing of oxygen level changes can be performed, for example, by coating the electrodes with one or more membranes of one or more polymers, such as NAFION™, in a Clark-type electrode setup or in a different configuration. Based on the change in potential, the change in oxygen concentration can be recorded, which correlates directly or indirectly with the concentration of alcohol. When properly designed to follow stoichiometric behavior, the presence of a certain concentration of alcohol should cause a corresponding decrease in local oxygen in a direct (linear) relationship with the concentration of alcohol. Thus, a multi-analyte sensor for both alcohol and oxygen can thus be provided.

[0278] In another embodiment, the alcohol sensing configuration described above can include one or more secondary enzymes that react with the reaction product of the alcohol / alcohol oxidase catalysis, e.g., hydrogen peroxide, to provide an oxidized form of the secondary enzyme that converts the alcohol-dependent signal to WE / RE at a lower potential than in the absence of the secondary enzyme. Thus, in one embodiment, the alcohol / alcohol oxidase is used with a reduced form of a peroxidase, e.g., horseradish peroxidase. The alcohol / alcohol oxidase can be in the same or different layer as the peroxidase, or they can be spatially separated distal to the electrode surface, e.g., the alcohol / alcohol oxidase is more distal from the electrode surface and the peroxidase is more proximal to the electrode surface, or alternatively, the alcohol / alcohol oxidase is more proximal from the electrode surface and the peroxidase is more distal from the electrode surface. In one embodiment, the alcohol / alcohol oxidase more distal to the electrode surface and peroxidase further comprises any combination of electrodes, interferences, resistors, and biointerface membranes to optimize signal, durability, reduce drift, or extend end of life duration.

[0279] In another embodiment, the alcohol sensing configuration described above can include one or more mediators. In one embodiment, the one or more mediators are present in, on, or around one or more electrodes or electrode surfaces and / or deposited on or otherwise associated with the surface of a working electrode (WE) or a reference electrode (RE). In one embodiment, the one or more mediators eliminate or reduce direct oxidation of interfering species that may reach the WE or RE. In one embodiment, the one or more mediators reduce the operating potential of the WE / RE to below about 0.6 V to about 0.3 V on a platinum electrode, for example, which can reduce or eliminate oxidation of endogenous interfering species. Examples of the one or more mediators are provided below. Other electrodes, such as counter electrodes, can be used.

[0280] In one embodiment, other enzymes or additional components may be added to the polymer mixture constituting any portion of the sensing region to increase the stability of the aforementioned sensor and / or reduce or eliminate by-products of the alcohol / alcohol oxidase reaction. Increased stability includes storage or shelf life and / or operational stability (e.g., retention of enzyme activity during use). For example, by-products of the enzyme reaction may be undesirable for increased shelf life and / or operational stability and therefore may be desirable to reduce or eliminate. In one embodiment, xanthine oxidase may be used to remove by-products of one or more enzyme reactions.

[0281] In another embodiment, a dehydrogenase enzyme is used with an oxidase for the detection of alcohol alone or in combination with oxygen. Thus, in one embodiment, an alcohol dehydrogenase is used to oxidize alcohol to an aldehyde in the presence of reduced nicotinamide adenine dinucleotide (NAD(P)H) or reduced nicotinamide adenine dinucleotide phosphate (NAD(P)+). To provide a continuous source of NAD(P)H or NAD(P)+, an NADH oxidase or an NADPH oxidase is used to oxidize NAD(P)H or NAD(P)+ while consuming oxygen. In another embodiment, diaphorase can be used in place of or in combination with the NADH oxidase or the NADPH oxidase. Alternatively, an excess amount of NAD(P)H can be incorporated into one or more enzyme domains and / or one or more electrodes in an amount to accommodate the intended duration of the planned life of the sensor.

[0282] In the aforementioned dual enzyme configuration, the signal can be sensed by either (1) electrically coupled (e.g., "wired") alcohol dehydrogenase (ADH), for example, using an electroactive hydrogel polymer containing one or more mediators, or (2) oxygen electrochemical sensing to measure the oxygen consumption of NADH oxidase. In an alternative embodiment, the cofactor NAD(P)H or NAD(P)+ can be bound to a polymer, such as dextran, which is immobilized to the enzyme domain along with ADH. This provides retention of the cofactor and its availability to the active site of ADH. In the above embodiments, any combination of electrodes, interferences, resistors, and biointerface membranes can be used to optimize the signal, durability, reduce drift, or extend the end of use duration. In one embodiment, direct or indirect electrical coupling via covalent or ionic bonds to at least a portion of the transducer element, such as an aptamer, enzyme, or cofactor, and at least a portion of the electrode surface is provided. Any chemical moiety capable of assisting electron transfer from the enzyme or cofactor to the electrode surface can be used, including one or more mediators as described below.

[0283] In one embodiment, any one of the aforementioned continuous alcohol sensor configurations is combined with any one of the aforementioned continuous ketone monitoring configurations to provide a continuous multi-analyte sensor device as described further below. In one embodiment, a continuous glucose monitoring configuration is combined with any one of the aforementioned continuous alcohol sensor configurations and any one of the aforementioned continuous ketone monitoring configurations to provide a continuous multi-analyte sensor device as described further below.

[0284] Uric acid sensor configuration In another embodiment, a continuous uric acid sensor device configuration is provided. Thus, in one embodiment, uric acid oxidase (UOX) can be included in one or more enzyme domains and located adjacent to the working electrode surface. Catalysis of uric acid with UOX produces hydrogen peroxide, which can be detected using amperometric, voltammetric and impedance measurement methods, among other techniques. In one embodiment, to reduce or eliminate interference from direct oxidation of uric acid on the electrode surface, one or more electrode, interference and / or resistance domains can be deposited on at least a portion of the working electrode surface. Such a membrane can be used to attenuate diffusion of uric acid as well as other analytes to the working electrode that may interfere with signal transduction.

[0285] In an alternative embodiment, the uric acid continuous sensing device configuration includes sensing oxygen level changes around the WE surface, for example, as in a Clark-type electrode setup, or one or more electrodes can independently include one or more different polymers, such as NAFION™, polyzwitterionic polymers, or polymeric mediators adjacent to at least a portion of the electrode surface. In one embodiment, an electrode surface having one or more electrode domains provides operation at different or lower voltages to measure oxygen. The oxygen level and its changes can be sensed, recorded, and correlated to the concentration of uric acid, for example, based on using conventional calibration methods.

[0286] In one embodiment, one or more coatings can be deposited on the WE surface to lower the potential at the WE for signaling of uric acid, either alone or in combination with any of the above-mentioned configurations, uric acid sensor configurations. The one or more coatings can be deposited or otherwise formed on the WE surface and / or on other coatings formed thereon using a variety of techniques, including but not limited to immersion, electrodeposition, vapor deposition, spray coating, and the like. In one embodiment, the coated WE surface can provide for a redox reaction, for example, of hydrogen peroxide, at a lower potential (compared to 0.6 V on a platinum electrode surface without such a coating). Examples of materials that can be coated or annealed on the WE surface include, but are not limited to, Prussian blue, Medola blue, methylene blue, methylene green, methyl viologen, ferrocyanide, ferrocene, cobalt ions, and cobalt phthalocyanine, and the like.

[0287] In one embodiment, one or more secondary enzymes, cofactors and / or mediators (electronically coupled or polymeric mediators) can be added to the enzyme domain with UOX to facilitate direct or indirect electron transfer to the WE. In such a configuration, for example, regeneration of the initial oxidized form of the secondary enzyme is reduced by the WE for signal transduction. In one embodiment, the secondary enzyme is horse radish peroxidase (HRP).

[0288] Choline Sensor Configuration In one embodiment, a continuous choline sensor device can be provided, for example, using a choline oxidase enzyme to generate hydrogen peroxide by oxidation of choline. Thus, in one embodiment, at least one enzyme domain includes choline oxidase (COX) adjacent to at least one WE surface, and optionally has one or more electrodes and / or interference membranes located between the WE surface and the at least one enzyme domain. Catalysis of choline with COX results in the creation of hydrogen peroxide, which can be detectable using amperometric, voltammetric, and impedancemetric methods, among other techniques.

[0289] In one embodiment, the continuous choline sensor configuration described above is combined with any one of the continuous alcohol sensor configuration and the continuous uric acid sensor configuration described above to provide a continuous multi-analyte sensor device as described further below. This continuous multi-analyte sensor device may further include continuous glucose monitoring capabilities. Other membranes may be used in the continuous choline sensor configuration described above, such as electrodes, resistors, biointerfaces, and drug-releasing membranes.

[0290] Cholesterol sensor configuration In one embodiment, a continuous cholesterol sensor configuration can be made using cholesterol oxidase (CHOX), similar to the sensors described above. Thus, one or more enzyme domains including CHOX can be located adjacent to at least one WE surface. Catalysis of free cholesterol with CHOX results in the creation of hydrogen peroxide, which can be detectable using amperometric, voltammetric, and impedimetric methods, among other techniques.

[0291] An exemplary cholesterol sensor configuration using platinum WE was prepared, in which at least one interference membrane is located adjacent to at least one WE surface, on which is located at least one enzyme domain containing CHOX, and on which is located at least one resistance domain for controlling the diffusion properties.

[0292] The method described above and the cholesterol sensor described above can measure free cholesterol, but with modification, this configuration can measure more types of cholesterol and total cholesterol concentration. Measuring different types of cholesterol and total cholesterol is important because a significant amount of cholesterol is in unmodified and esterified form due to the low solubility of cholesterol in water. Thus, in one embodiment, a total cholesterol sample is provided in which a secondary enzyme is introduced into at least one enzyme domain, for example to provide a combination of cholesterol esterase and CHOX cholesteryl ester, which is converted from the cholesterol present and essentially represents the total cholesterol that can be measured indirectly from the signal formed by esterase.

[0293] In one embodiment, the continuous (total) cholesterol sensor configuration described above is combined with any one of the continuous alcohol sensor configurations and / or continuous uric acid sensor configurations described above to provide a continuous multi-analyte sensor system as described further below. This continuous multi-analyte sensor device may further include continuous glucose monitoring capabilities. Other membrane configurations may be used in the continuous cholesterol sensor configuration described above, such as one or more electrode domains, resistive domains, biointerface domains, and drug-releasing membranes.

[0294] Configuration of bilirubin sensor and ascorbic acid sensor In one embodiment, continuous bilirubin and ascorbic acid sensors are provided. These sensors can use bilirubin oxidase and ascorbic acid oxidase, respectively. However, unlike some oxidoreductase enzymes, the end product of catalysis of analytes of bilirubin oxidase and ascorbic acid oxidase is water instead of hydrogen peroxide. Therefore, hydrogen peroxide redox detection to correlate with bilirubin or ascorbic acid is not possible. However, these oxidase enzymes still consume oxygen for catalysis, and the level of oxygen consumption correlates with the level of target analyte present. Therefore, bilirubin and ascorbic acid levels can be measured indirectly by electrochemically sensing oxygen level changes, for example, in a Clark-type electrode setup.

[0295] Alternatively, different configurations for sensing bilirubin and ascorbic acid can be used. For example, an electrode domain including one or more electrode domains including an electron transfer agent such as NAFION™, a polyzwitterionic polymer, or a polymeric mediator can be coated onto the electrode. Measured oxygen levels converted from such enzyme domain configurations can be correlated with bilirubin concentrations and ascorbic acid levels. In one example, an electrode domain including one or more mediators electrically coupled to the working electrode can be used and correlated with bilirubin levels and ascorbic acid levels.

[0296] In one embodiment, the continuous bilirubin and ascorbic acid sensor configuration described above may be combined with any one of the continuous alcohol, uric acid, and cholesterol sensor configurations described above to provide a continuous multi-analyte sensor device as described further below. This continuous multi-analyte sensor device may further include continuous glucose monitoring capabilities. Other membranes may be used in the continuous bilirubin and ascorbic acid sensor configurations described above, such as electrodes, resistors, biointerfaces, and drug-releasing membranes.

[0297] One working electrode configuration for dual analyte detection In one embodiment, at least a dual enzyme domain configuration is provided, with each layer containing one or more specific enzymes and optionally one or more cofactors. In a broader sense, one embodiment of a continuous multi-analyte sensor configuration is depicted in FIG. 9A, where a first membrane 855 (EZL1) containing at least one enzyme (Enzyme 1) of at least two enzyme domain configurations is adjacent to at least one surface of a WE. One or more analyte-substrate enzyme pairs with Enzyme 1 convert at least one detectable signal to the WE surface by direct or mediated electron transfer that corresponds directly or indirectly to the analyte concentration. A second membrane 856 (EZL2) with at least one second enzyme (Enzyme 2) is located adjacent to 855 ELZ1 and is generally more distal from the WE than EZL1. One or more resistance domains (RL) 852 may be provided adjacent to EZL2 856 and / or between EZL1 855 and EZL2 856. Although the different enzymes catalyze the conversion of the same analyte, at least one enzyme in EZL2 856 provides hydrogen peroxide and at least one other enzyme in EZL1 855 does not provide hydrogen peroxide. Thus, each measurable species (e.g., hydrogen peroxide and the other measurable species that is not hydrogen peroxide) produces a signal related to its concentration.

[0298] For example, in the configuration shown in Figure 9A, a first analyte diffuses through RL 852 into EZL2 856 and provides peroxide through interaction with Enzyme 2. The peroxide diffuses through at least EZL1 855 to the WE and transduces a signal that corresponds directly or indirectly to the first analyte concentration. A second analyte, different from the first analyte, diffuses through RL 852 and EZL2 856 and interacts with Enzyme 1, which results in electron transfer to the WE and transduces a signal that corresponds directly or indirectly to the second analyte concentration.

[0299] As shown in Figure 9B, the above configuration is adapted for conductive wire electrode construction, where at least two different enzyme-containing layers are constructed on the same WE with a single active surface. In one embodiment, the single WE is a wire, and the active surface is located around the longitudinal axis of the wire. In another embodiment, the single WE is a conductive trace on a substrate, and the active surface is located around the longitudinal axis of the trace. In one embodiment, the active surface is substantially continuous around the longitudinal axis or radius.

[0300] In the configuration described above, at least two different enzymes are used that can catalyze the conversion of different analytes, with at least one enzyme in EZL2 856 providing hydrogen peroxide and at least another enzyme in EZL1 855 not providing hydrogen peroxide but providing electron transfer to the WE surface that corresponds directly or indirectly to the concentration of the analyte, for example.

[0301] In one embodiment, the inner layer of at least two enzyme domains EZL1, EZL2 855, 856 includes at least one immobilized enzyme in combination with at least one mediator that can facilitate lower bias voltage operation of the WE than without the mediator. In one embodiment, an electric potential P1 is used for such direct electron conversion. In one embodiment, at least a portion of the inner layer EZL1 855 is closer to the WE surface and may have one or more intervening electrode domains and / or overlay interferences and / or biological interfaces and / or drug release membranes, provided that at least one mediator can facilitate lower bias voltage operation with the WE surface. In another embodiment, at least a portion of the inner layer EZL1 855 is directly adjacent to the WE.

[0302] The second layer (outer layer EZL2 856) of at least the dual enzyme domain in FIG. 9B contains at least one enzyme that results in one or more catalytic reactions that ultimately produce an amount of hydrogen peroxide that can be electrochemically converted into a signal corresponding to the concentration of an analyte. In one embodiment, the generated hydrogen peroxide diffuses through layer EZL2 856 and through inner layer EZL1 855, reaches the WE surface, and undergoes oxidation-reduction at a potential of P2, where P2≠P1. In this way, electron transfer and electrolysis (reduction-reduction) can be selectively controlled by controlling the potentials P1, P2 applied to the same WE surface. Any applied potential duration, e.g., equal / periodic duration, time-staggered duration, random duration, as well as various potential difference sequences, cyclic voltammetry, etc., can be used for P1, P2. In some embodiments, impedance measurement sensing can be used. In one example, the phase shift (e.g., time lag) may result from detecting two signals from two different working electrodes, each signal generated by a different EZL (EZL1 855, EZL2 856) associated with each electrode. The two (or more) signals may be resolved into components to detect the individual signals and signal artifacts generated by each of EZL1 855 and EZL2 856 in response to detection of the two analytes. In some examples, each EZL detects a different analyte. In other examples, both EZLs detect the same analyte.

[0303] In another alternative exemplary configuration, the multienzyme domain configuration described above is provided for a continuous multi-analyte sensor device using a single WE with two or more active surfaces, as shown in Figures 9C and 9D. In one embodiment, the multienzyme domain configuration discussed herein is formed on a planar substrate. In another embodiment, the single WE is coaxial, e.g., configured as a wire, with two or more active surfaces located around the longitudinal axis of the wire. The additional wires can be used, e.g., as reference and / or counter electrodes. In another embodiment, the single WE is a conductive trace on a substrate, with two or more active surfaces located around the longitudinal axis of the trace. At least a portion of the two or more active surfaces are discontinuous, providing at least two physically separated WE surfaces on the same WE wire or trace. (e.g., WE1, WE2). In one embodiment, the first analyte detected by WE1 is glucose and the second analyte detected by WE2 is lactate. In another embodiment, the first analyte detected by WE1 is glucose and the second analyte detected by WE2 is a ketone.

[0304] Thus, Figures 9C and 9D depict an exemplary configuration of a continuous multi-analyte sensor configuration in which EZL1 855, EZL2 856, and RL852 (resistance domains) as described above are disposed, for example, by sequential dip coating techniques, on a single coaxial wire that includes spatially separated electrode surfaces WE1, WE2. One or more parameters, such as the enzyme domain, the resistance domain, etc., can be independently controlled along the longitudinal axis of the WE, for example, thickness, length along the axis from the distal end of the wire, etc. In one embodiment, at least a portion of the spatially separated electrode surfaces are of the same composition. In another embodiment, at least a portion of the spatially separated electrode surfaces are of different compositions. In Figures 9C and 9D, WE1 represents a first working electrode surface, for example, configured to operate at P1, electrically isolated from a second working electrode surface WE2, configured to operate at P2, and RE represents a reference electrode RE that is electrically isolated from both WE1, WE2. In the configuration of FIG. 9C, one resistive domain is provided covering the reference electrode and WE1, WE2. In the configuration of FIG. 9D, an additional resistive domain is provided that essentially extends only over WE2. Additional electrodes, such as a counter electrode, may be used. Such configurations (whether single-wire or dual-wire configurations) may also be used to measure the same analyte using two different techniques. Data collected from two different measurement modes using different signal generation sequences as well as different RLs provides increased fidelity, improved performance, and device life. A non-limiting example is a glucose oxidase (H2O2 producing) and glucose dehydrogenase (electrically coupled) configuration. Measurement of glucose from two different electrodes at two potentials provides more data points and precision. Such an approach may not be required for glucose sensing, but may be applied across the biomarker sensing spectrum of other analytes, alone or in combination with glucose sensing such as ketone sensing, ketone / lactate sensing, and ketone / glucose sensing.

[0305] In an alternative configuration to that depicted in FIG. 9C and FIG. 9D, two or more wire electrodes are presented that may be collinear, wrapped, or otherwise juxtaposed, with WE1 being separated from WE2, e.g., from another elongated shaped electrode. An insulating layer electrically insulates WE1 from WE2. In this configuration, independent electrode potentials may be applied to the corresponding electrode surfaces, and independent electrode potentials may be provided to WE1, WE2 simultaneously, sequentially, or randomly. In one embodiment, the electrode potentials provided to the corresponding electrode surfaces WES1, WES2 are different. One or more additional electrodes, such as a reference electrode and / or a counter electrode, may be present. In one embodiment, WES2 is located longitudinally distal from WES1 in an elongated configuration. For example, using a dip coating method, WES1 and WES2 are coated with an enzyme domain EZL1, while WES2 is coated with a different enzyme domain EZL2. Multi-layer enzyme domains, with each layer independently containing a different loading and / or composition of enzyme and / or cofactor, mediator, based on immersion parameters, or different thicknesses of the enzyme domain, can be used. Similarly, one or more resistance domains (RLs) can be applied, each of different thickness along the longitudinal axis of the electrode, across different electrodes and enzyme domains, for example, by controlling immersion length and other parameters. Referring to FIG. 9D, such an arrangement of RLs is depicted, with an additional RL852' adjacent to WES2, but substantially absent from WES1.

[0306] In one embodiment for measuring two different analytes, the above configuration includes an enzyme domain EZL1 855 that includes one or more enzymes and one or more mediators for at least one enzyme of EZL1 to provide direct electron transfer to WES1 to determine the concentration of at least a first analyte. Additionally, an enzyme domain EZL2 856 can include at least one enzyme that provides peroxide (e.g., hydrogen peroxide) or consumes oxygen during catalysis with its substrate. The peroxide or oxygen produced in EZL2 856 is transferred to WES2 to provide a detectable signal directly or indirectly corresponding to a second analyte. For example, WES2 can be carbon wired to glucose dehydrogenase to measure glucose, while WES1 can be platinum to measure the peroxide produced from lactate oxidase / lactate in EZL2 856. The combinations of electrode materials and enzymes disclosed herein are examples and are non-limiting.

[0307] In one example, the P1 and P2 potentials can be separated by an amount of potential so that both signals (from direct electron transfer from EZL1 855 and from hydrogen peroxide redox at the WE) can be activated and measured separately. In one example, the sensor's electronic module can continuously switch between the two sensing potentials in a continuous or semi-continuous cyclic manner, for example, for a period (t1) at potential P1 and a period (t2) at potential P2, optionally with a rest period during which no potential is applied. The extracted signals can then be analyzed to measure the concentrations of two different analytes. In another example, the sensor's electronic module can undergo cyclic voltammetry, and the change in current when swiping across the P1 and P2 potentials can be correlated to the transduced signal coming from either direct electron transfer or hydrogen peroxide electrolysis, respectively. In one example, the sensing modality is non-limiting and can include different amperometric techniques, for example, cyclic voltammetry. In one embodiment, an alternative configuration is provided in which hydrogen peroxide production in EZL2 is replaced by another suitable electrolytic compound that maintains the P2 ≠ P1 relationship, such as oxygen, and at least one enzyme-substrate combination that provides the other electrolytic compound.

[0308] For example, a continuous multi-analyte sensor configuration for choline and glucose was prepared in which enzyme domains EZ1 855, EZ2 856 were associated with different WEs, e.g., platinum WE2 and gold WE1. In this exemplary case, EZL1 855 contained glucose oxidase and a mediator bound to WE1 to facilitate direct transfer of electrons upon catalysis of glucose, and EZL2 856 contained choline oxidase that catalyzed choline to generate hydrogen peroxide for electrolysis at WE2. The EZLs were coated with resistive domains, and upon curing and preparation, they underwent cyclic voltammetry in the presence of glucose and choline. The glucose oxidase enzyme wired to the gold electrode was capable of transducing a signal at 0.2 volts, and thus, by analyzing the current change at 0.2 volts, glucose concentration could be determined. The data also demonstrates that choline concentration is also inferentially detectable at the WE2 platinum electrode when the CV trace is analyzed at voltage P2.

[0309] In one embodiment, either electrode WE1 or WE2 can be, for example, a composite material, such as a gold electrode with platinum ink deposited on top, a carbon / platinum mixture, and / or traces of carbon on top of platinum, or a porous carbon coating on a platinum surface. In one embodiment, an electrode surface can contain two different materials, for example, a wired enzyme and carbon used for electron transfer, while platinum can be used for oxidation-reduction and detection of hydrogen peroxide. As shown in FIG. 9E, an example of such a composite electrode surface is shown, where an extended platinum-coated wire 857 is half-coated with carbon 858 to facilitate multiple sensing on two different surfaces of the same electrode. In one embodiment, WE2 can be grown on or extend from a portion of the surface or distal end of WE1, such as by evaporation, sputtering, or electrolytic deposition.

[0310] Further examples include composite electrode materials that can be used to form one or both of WE1 and WE2. In one example, a platinum-carbon electrode WE1 containing EZL1 with glucose dehydrogenase wired to the carbon surface and an outer EZL2 containing lactate oxidase that produces hydrogen peroxide detectable by the platinum surface of the same WE1 electrode. Other examples of this configuration can include ketone sensing (beta-hydroxybutyrate dehydrogenase electrocoupled enzyme in EZL1 855) and glucose sensing (glucose oxidase in EZL2 856). Other membranes can be used in the above configurations, such as electrodes, resistors, biointerfaces, and drug release membranes. In other examples, one or both of the working electrodes (WE1, WE2) can be gold-carbon (Au-C), palladium-carbon (Pd-C), iridium-carbon (Ir-C), rhodium-carbon (Rh-C), or ruthenium-carbon (Ru-C). In some examples, the carbon in the working electrodes discussed herein may alternatively or additionally include other materials suitable for forming working electrodes, such as graphene, graphene oxide, or commercially available carbon inks.

[0311] Glycerol Sensor Configuration As shown in FIG. 10A, an exemplary continuous glycerol sensor configuration is depicted in which a first enzyme domain EZL1 860, which includes galactose oxidase, is located proximate to at least a portion of the WE surface. A second enzyme domain EZL2 861, which includes glucose oxidase and catalase, is located more distally from the WE. As shown in FIG. 10A, one or more resistance domains (RLs) 852 are located between EZL1 860 and EZL2 861. Additional RLs can be used, for example, adjacent to EZL2 861. Modifications of one or more RL membranes to attenuate the flux of any analyte and increase the glycerol to galactose sensitivity ratio are envisioned. The above glycerol sensing configuration provides a glycerol sensor that can be combined with one or more additional sensor configurations as disclosed herein.

[0312] Glycerol can be catalyzed by the enzyme galactose oxidase (GalOx), which has an activity ratio of 1%-5% relative to glycerol. In one embodiment, the activity of GalOx on this secondary analyte glycerol can be exploited. The relative concentration of glycerol in vivo is much higher than that of galactose (approximately 2 μmol / l for galactose and approximately 100 μmol / l for glycerol), which complements the above configuration.

[0313] If the GalOx present in the EZL1 860 membrane is not otherwise functionally limiting, it will catalyze most, if not all, of the glycerol passing through one or more RLs. The signal contribution from the glycerol present is higher compared to the signal contribution from galactose. In one embodiment, one or more RLs are chemically configured to provide a higher flux of glycerol or a lower flux of galactose.

[0314] In another example, a glycol sensor configuration is provided using multiple working electrode WEs that provide for utilizing signals converted from both WEs. Utilizing signals converted from both WEs can provide increased selectivity. In one example, EZL1 860 and EZL2 861 contain the same oxidase enzyme (e.g., galactose oxidase) with different ratios of enzyme loading and / or different immobilization polymers and / or different numbers and layers of RLs on the WE. Such a configuration provides for measurement of the same target analyte with different sensitivities, resulting in dual measurements. Using a mathematical algorithm to correct for noise and interference from a first signal, and inputting a first signal from one sensing electrode with a first analyte sensitivity ratio into the mathematical algorithm allows for decoupling of a second signal corresponding to a desired analyte contribution. Modification of the sensitivity ratio of one or more EZLs to distinguish between interference species and signals from the analyte of interest can be provided by adjusting one or more of the enzyme source, enzyme loading in the EZL, the chemistry / diffusion properties of the EZL, the chemistry / diffusion properties of at least one RL, and combinations thereof.

[0315] As discussed herein, a secondary enzyme domain may be utilized to catalyze non-target analytes, reducing their concentration and limiting their diffusion through the adjacent membrane containing the primary enzyme and necessary additives toward the sensing electrode. In this example, the most distal enzyme domain, EZL2, 861, is configured to catalyze non-target analytes that would otherwise react with EZL1, thus providing a potentially less accurate reading of the target analyte (glycerol) concentration. This secondary enzyme domain may act as a "selective diffusion exclusion membrane" by itself, or in some other configurations, may be placed above or below the resistive layer (RL) 852. In this example, the target analyte is glycerol, and GalOx is used to catalyze the glycerol to form a measurable species (e.g., hydrogen peroxide).

[0316] In one embodiment, a continuous glycerol sensor configuration is provided using at least glycerol oxidase, which provides hydrogen peroxide upon reaction and catalysis of glycerol. Thus, in one embodiment, an enzyme domain including glycerol oxidase can be located adjacent to at least a portion of a WE surface, and hydrogen peroxide is detected using amperometry. In another embodiment, an enzyme domain including glycerol oxidase is used to sense oxygen level changes, for example, in a Clark-type electrode setup. Alternatively, at least a portion of the WE surface can be coated with one or more layers of electrically coupled polymers, such as the mediator system discussed below, to provide a coated WE in which electron transfer from the enzyme is possible at a lower potential. The coated WE can then be operated at a different lower voltage to measure its correlation to oxygen and glycerol concentration.

[0317] In another embodiment, a glycerol sensor configuration is provided using glycerol-3-phosphate oxidase in the enzyme domain. In one embodiment, ATP is used as a cofactor. Thus, as shown in FIG. 10B and FIG. 10C, an exemplary sensor configuration is depicted, where in one embodiment FIG. 10B, one or more cofactors (e.g., ATP) 862 are proximal to at least a portion of the WE surface. One or more enzyme domains 863 including glycerol-3-phosphate oxidase (G3PD), lipase, and / or glycerol kinase (GK), and one or more regenerating enzymes capable of continuously regenerating the cofactors are included in the enzyme domains adjacent to the cofactors or more distal from the WE surface than the cofactor layer 862. Examples of regenerating enzymes that can be used to provide ATP regeneration include, but are not limited to, ATP synthase, pyruvate kinase, acetate kinase, and creatine kinase. The one or more regenerating enzymes may be contained in one or more enzyme domains or in separate layers.

[0318] An alternative configuration is shown in Figure 10C, where one or more enzyme domains 863 containing G3PD, at least one cofactor, and at least one regenerating enzyme are located proximate to at least a portion of the WE surface, one or more cofactor reservoirs 862 are adjacent to the enzyme domain containing G3PD and more distal to the WE surface, and one or more RLs 852 are located adjacent to the cofactor reservoirs. In any of these configurations, an additional enzyme domain containing a lipase can be included to indirectly measure triglycerides, as the lipase produces glycerol for detection by the glycerol sensor configuration described above.

[0319] In another embodiment, a glycerol sensor configuration is provided using a dehydrogenase enzyme with a cofactor and a regenerating enzyme. In one embodiment, the cofactors that can be incorporated into one or more enzyme domains include one or more of NAD(P)H, NADP+, and ATP. In one embodiment, for example, for the use of NAD(P)H, the regenerating enzyme can be NADH oxidase or diaphorase to convert NADH, the product of dehydrogenase catalysis, back to NAD(P)H. Similar methodologies can be used to create other glycerol sensors, for example, glycerol dehydrogenase combined with NADH oxidase or diaphorase can be configured to measure glycerol or oxygen.

[0320] In one embodiment, mathematical modeling can be used to identify and remove interfering signals, measure very low analyte concentrations, signal error and noise reduction to improve and increase end-of-life of a multi-analyte sensor. For example, using a two WE electrode configuration where WE1 is coated with a first EZL and WE2 is coated with two or more different EZLs, optionally using one or more resistive domains (RL), mathematical correction of such interferences can be corrected to provide for increased accuracy of the measurement.

[0321] Variations in enzyme loading, immobilization polymer and resistance domain properties across each analyte sensing region can result in different sensitivity ratios between two or more target analytes and interfering species. When signals are collected and analyzed using mathematical modeling, more accurate concentrations of the target analytes can be calculated.

[0322] One example where the use of mathematical modeling can be useful is glycerol sensing, where galactose oxidase is sensitive to both galactose and glycerol. The sensitivity ratio of galactose oxidase to glycerol is about 1%-5% of its sensitivity to galactose. In such a case, modification of the sensitivity ratio to the two analytes is possible by adjusting one or more parameters such as the enzyme source, enzyme loading, enzyme domain (EZL) diffusion properties, RL diffusion properties, and combinations thereof. When two WEs are operating in a sensor system, signal correction and analysis from both WEs using mathematical modeling provides a high degree of fidelity and target analyte concentration measurement.

[0323] In the above configurations, the proximity to the WE of one or more of these enzyme immobilization layers discussed herein may be different or reversed, for example, a configuration may be used where the one closest to the WE enzyme domain provides hydrogen peroxide.

[0324] In some examples, the target analyte may be measured using one or more enzymes working in concert. In one example, ATP may be immobilized in one or more EZL membranes, or added to an adjacent layer alone or in combination with a secondary cofactor, or may be regenerated / recycled for use in the same EZL or an adjacent third EZL. This configuration may further include a cofactor regenerating enzyme, for example, alcohol dehydrogenase or NADH oxidase to regenerate NAD(P)H. Other examples of cofactor regenerating enzymes that may be used to regenerate ATP are ATP synthase, pyruvate kinase, acetate kinase, creatine kinase, etc.

[0325] In one embodiment, the continuous glycerol sensor configuration described above may be combined with any one of the continuous alcohol, uric acid, cholesterol, bilirubin / ascorbic acid, ketone, and choline sensor configurations described above to provide a continuous multi-analyte sensor device as described further below. This continuous multi-analyte sensor device may further include continuous glucose monitoring capabilities. Other configurations may be used in the continuous glycerol sensor configuration described above, such as electrodes, resistors, biointerfaces, and drug-releasing membranes.

[0326] Creatinine Sensor Configuration In one embodiment, a continuous creatinine sensor configuration is provided, which contains one or more enzymes and / or cofactors. The creatinine sensor configuration is an example of a continuous analyte sensing system that generates intermediate, interfering products, and these intermediates / interfering substances are also present in the sampled biological fluid. The present disclosure provides a solution to address these technical issues, and provides accurate and stable continuous creatinine monitoring, alone or in combination with other continuous multi-analyte sensor configurations.

[0327] Creatinine sensors undergo a number of physiologically occurring intermediate / interfering products, such as sarcosine and creatine, which may affect the correlation between the converted signal and creatinine concentration during use. For example, the physiological concentration range of sarcosine is an order of magnitude lower than that of creatinine or creatine, so the signal contribution from circulating sarcosine is typically minimal. However, changes in local physiological creatine concentration can affect the creatinine sensor signal. In one embodiment, it is provided to eliminate or reduce such signal contribution.

[0328] Thus, in one embodiment, eliminating or reducing the creatine signal contribution of a creatinine sensor includes using at least one enzyme that consumes a non-target interfering analyte, in this case creatine. For example, two enzyme domains located adjacent to each other are used. At least a portion of the first enzyme domain is located proximal to at least a portion of the WE surface, and the first enzyme domain includes one or more enzymes selected from creatinine amidohydrolase (CNH), creatine amidohydrolase (CRH), and sarcosine oxidase (SOX). A second enzyme domain adjacent to the first enzyme domain and more distal to the WE surface includes one or more enzymes that use creatine as their substrate so as to eliminate or reduce creatine diffusion toward the WE. In one example, the enzyme combination includes CRH, SOX, creatine kinase, and catalase, and the enzyme ratio is adjusted to provide a sufficient number of units such that circulating creatine is at least partially consumed by CRH to provide sarcosine and urea, while the sarcosine produced is at least partially consumed by SOX to provide an oxidized form of glycine (e.g., glycinaldehyde) that is at least partially consumed by catalase. In the above alternative configuration, the urea produced by CRH catalysis can be at least partially consumed by urease to provide ammonia, and the aqueous form (NH4+) is detected via an ion-selective electrode (e.g., a non-actin ionophore). Such an alternative potentiometric sensing configuration may provide an alternative to amperometric peroxide detection (e.g., improved sensitivity, detection limits, and lack of depletion of the reference electrode, alternative pathways / mechanisms). This dual analyte sensing example may include a creatinine-potassium sensor with potentiometric sensing at two different working electrodes. In this example, interference signals may be identified and corrected.In an alternative embodiment, the above configuration may include a multi-modal sensing architecture that uses a combination of amperometry and potentiometry to detect concentrations of peroxide and ammonium ions, which are measured using amperometry and potentiometry, respectively, and correlated to measure the concentration of creatinine. In one embodiment, the above configuration may further include one or more configurations (e.g., no enzyme) that separate the two enzyme domains to provide complementary or supplemental diffusion separations and barriers.

[0329] In yet another embodiment, a method for isolating the signal and essentially measuring only creatinine is to use a second WE that measures an interfering species (e.g., creatine) and then corrects the signal using mathematical modeling. Thus, for example, a signal from a WE that interacts with creatinine is used as a reference signal. A signal from another WE that interacts with creatinine is then corrected for the signal from the WE that interacts with creatinine to selectively determine the creatinine concentration.

[0330] In yet another embodiment, creatinine sensing is provided by electrochemically measuring oxygen level changes, for example in a Clark-type electrode setup, or by using one or more electrodes coated with layers of different polymers, such as NAFION™, to correlate changes in electrical potential based on oxygen changes, which indirectly correlate with the concentration of creatinine.

[0331] In yet another embodiment, creatinine sensing is provided by using sarcosine oxidase wired to at least one WE using one or more electrically coupled mediators, in which the concentration of creatinine is indirectly correlated to the electron transfer generated signal collected from the WE.

[0332] For the aforementioned creatinine sensor configurations based on hydrogen peroxide and / or oxygen measurements, the one or more enzymes can be in a single enzyme domain, or the one or more enzymes can be independently in one or more enzyme domains, or any other combination thereof with at least one enzyme in each layer. For the aforementioned creatinine sensor configurations based on the use of an electrically coupled sarcosine oxidase-containing layer, the layer is located adjacent to the electrode and is electrically coupled to at least a portion of the electrode surface using a mediator.

[0333] In another embodiment, the creatinine sensor configuration described above can be sensed using potentiometry by using urease enzyme (UR) to create ammonium from urea, which is created by CRH from creatinine, which is formed from the interaction of creatinine with CNH. Thus, ammonium can be measured by the above configuration and correlated with creatinine concentration. Alternatively, creatine amidohydrolase (CI) or creatinine deiminase can be used to create ammonia gas, which provides ammonium ions for signaling under physiological conditions of the transdermal sensor.

[0334] In yet another embodiment, sensing of creatinine is provided by using one or more enzymes and one or more cofactors. Some non-limiting examples of such configurations include creatinine deaminase (CD) which provides ammonium from creatinine, and glutamate dehydrogenase (GLDH) which provides peroxide from ammonium, where hydrogen peroxide correlates with the level of creatinine present. The above configurations can further include a third enzyme, glutamate oxidase (GLOD), to further degrade glutamate formed from GDLH and create additional hydrogen peroxide. Such combinations of enzymes can be in one or more enzyme domains, independently, or any other combination thereof, with at least one enzyme present in each domain or layer.

[0335] In yet another embodiment, creatinine sensing is provided by a combination of creatinine amidohydrolase (CNH), creatine kinase (CK) and pyruvate kinase (PK), and pyruvate produced by PK can be detected by any one or more of the independently configured lactate dehydrogenase (LDH) or pyruvate oxidase (POX) enzymes, where one or more of the aforementioned enzymes are present in one layer, or each of multiple layers contains at least one enzyme, any other combination thereof.

[0336] In such sensor configurations where one or more cofactors and / or regenerating enzymes for the cofactors are used, providing an excess of one or more of NADH, NAD(P)H and ATP in any one of one or more configurations may be used and one or more diffusion resistance domains may be introduced to limit or prevent the flux of the cofactors from their respective membranes. Other configurations may be used in the aforementioned configurations, such as electrodes, resistors, biointerfaces, and drug-releasing membranes.

[0337] In yet another embodiment, creatinine detection is provided by using creatinine deiminase in one or more enzyme domains and providing ammonium to the enzyme domains through the catalytic action of creatinine.The ammonium ion can then be detected potentiometrically or by using a composite electrode that undergoes redox when exposed to ammonium ion, such as a NAFION™ / polyaniline composite electrode, where polyaniline undergoes redox in the presence of ammonium at the electrode under potential.The ammonium concentration can then be correlated with the creatinine concentration.

[0338] FIG. 11 illustrates an exemplary continuous sensor configuration for creatinine. In the example of FIG. 11, the sensor includes a first enzyme domain 864 including CNH, CRH, and SOX adjacent to a working electrode WE, e.g., platinum. A second enzyme domain 865 is located adjacent to the first enzyme domain and more distal from the WE. One or more resistance domains (RLs) 852 can be located adjacent to the second enzyme domain or between the first and second layers. Creatinine can diffuse through the RL and the second enzyme domain to the first enzyme domain, where it is converted to peroxide and converts a signal corresponding to its concentration. Creatine can diffuse through the RL and is converted to sarcosine and urea in the second enzyme domain, where sarcosine is consumed by sarcosine oxidase and the generated peroxide is consumed by catalase, thus preventing the transmission of the creatine signal.

[0339] For example, variations of the above configurations are possible for continuous monitoring of creatinine alone or in combination with one or more other analytes. Thus, one alternative approach to sensing creatinine may be electrochemical sensing of oxygen level changes, for example, in a Clark-type electrode setup. In one example, the WE may be coated with layers of different polymers, such as NAFION™, and the concentration of creatinine may be correlated based on changes in potential oxygen changes. In yet another example, one or more enzymes closest to the WE, i.e., sarcosine oxidase, may be "wired" to the electrode using one or more mediators. Each of the different enzymes in the above configurations may be distributed within a polymer matrix or domain to provide one enzyme domain. In another example, one or more of the different enzymes discussed herein may be formed as enzyme domains, with each layer having at least one enzyme present. In an example of a "wired" enzyme configuration with a multi-layer membrane, the wired enzyme domain is closest to the electrode. One or more interferent layers may be deposited between the multi-layer enzyme configurations to block non-target analytes from reaching the electrode.

[0340] In one embodiment, the continuous creatinine sensor configuration described above may be combined with any one of the continuous alcohol, uric acid, cholesterol, bilirubin / ascorbic acid, ketone, choline, and glycerol sensor configurations described above to provide a continuous multi-analyte sensor device as described further below, which may further include continuous glucose monitoring capability.

[0341] Lactose sensor configuration In one embodiment, a continuous lactose sensor configuration is provided, alone or in combination with another analyte sensing configuration comprising one or more enzymes and / or cofactors. In a general sense, a lactose sensing configuration using at least one enzyme domain comprising lactase enzyme is used to produce glucose and galactose from lactose. The produced glucose or galactose is then enzymatically converted to peroxide for signaling at an electrode. Thus, in one embodiment, at least one enzyme domain EZL1 comprising lactase is located proximate to at least a portion of the WE surface capable of electrolysis of hydrogen peroxide. In one embodiment, a glucose oxidase enzyme (GOX) is included in the EZL1 together with one or more cofactors or electrically coupled mediators. In another embodiment, a galactose oxidase enzyme (GalOx) is included in the EZL1, optionally together with one or more cofactors or mediators. In one embodiment, the glucose oxidase enzyme and galactose oxidase are both included in the EZL1. In one embodiment, both the glucose oxidase enzyme and the galactose oxidase are included in EZL1, optionally with one or more cofactors or electrically coupled mediators.

[0342] One or more additional EZLs (e.g., EZL2) can be located adjacent to EZL1, with at least a portion of EZL2 being more distal from at least a portion of the WE than EZL1. In one embodiment, one or more layers can be located between EZL1 and EZL2, and such layers can include enzymes, cofactors, or mediators, or can essentially lack one or more of the enzymes, cofactors, or mediators. In one embodiment, one or more layers located between EZL1 and EZL2 are essentially lacking enzymes, e.g., do not include intentionally added enzymes. In one embodiment, one or more layers can be located adjacent to EZL2, and are more distal from at least a portion of EZL1 than EZL2, and include one or more enzymes present in either EZL1 or EZL2.

[0343] In one embodiment of the lactose sensor configuration, the peroxide generating enzyme can be electrically coupled to an electrode using a binding mediator. The transduced peroxide signal from the lactose sensor configuration can be correlated with the level of lactose present.

[0344] 12A-12D depict an alternative continuous lactose sensor configuration. Thus, in the enzyme domain EZL1 864 closest to WE(G1), which contains GalOx and lactase, a lactose sensor is provided that is sensitive to galactose and lactose concentration changes and does not essentially transduce glucose concentration. As shown in FIG. 12B-12D, additional layers (not shown) are used, including a non-enzyme-containing layer 859, a lactase enzyme-containing layer 865, and optionally electrodes, resistors, biointerfaces, and drug-releasing membranes. Because the changes in physiological galactose concentration are minimal, the transduced signal is essentially from physiological lactose fluctuations.

[0345] In one embodiment, the continuous lactose sensor configuration described above may be combined with any one of the continuous alcohol, uric acid, cholesterol, bilirubin / ascorbic acid, ketone, choline, glycerol, and creatinine sensor configurations described above to provide a continuous multi-analyte sensor device as described further below. This continuous multi-analyte sensor device may further include continuous glucose monitoring capabilities. Other membranes may be used in the sensor configurations described above, such as electrodes, resistors, biointerfaces, and drug-releasing membranes.

[0346] Urea Sensor Configuration Similar approaches to those described above can also be used to create a continuous urea sensor. For example, urease (UR), which can decompose urea to provide ammonium, can be used in an enzyme domain configuration. Ammonium can be detected potentiometrically or by using a composite electrode, such as an electrode that undergoes redox when exposed to ammonium. Exemplary electrodes for ammonium signaling include, but are not limited to, NAFION™ / polyaniline composite electrodes, where polyaniline undergoes redox in the presence of ammonium at an applied potential, with an essentially direct correlation between the signal and the level of ammonium present in the environment. This method can also be used to measure other analytes, such as glutamic acid, using the enzyme glutaminase (GLUS).

[0347] In one embodiment, the above-mentioned continuous uric acid sensor configuration may be combined with any one of the above-mentioned continuous alcohol sensor configurations and / or continuous uric acid sensor configurations and / or continuous cholesterol sensor configurations and / or continuous bilirubin / ascorbic acid sensor configurations and / or continuous ketone sensor configurations and / or continuous choline sensor configurations and / or continuous glycerol sensor configurations and / or continuous creatinine sensor configurations and / or continuous lactose sensor configurations to provide a continuous multi-analyte sensor device as described further below. This continuous multi-analyte sensor device may further include continuous glucose monitoring capabilities. Other membranes may be used in the above-mentioned uric acid sensor configurations, such as electrodes, resistors, biointerfaces, and drug-releasing membranes.

[0348] In certain embodiments, the continuous analyte monitoring system 104 can be a lactate sensor, as discussed with reference to Figure 1. Figures 13A-14C describe an exemplary lactate sensor system used to measure lactate, according to certain embodiments of the present disclosure.

[0349] FIG. 13A shows one exemplary embodiment of the physical structure of a lactate sensor 1338. In this embodiment, a radial window 1303 is formed through an insulating layer 1305 to expose an electroactive working electrode of conductive material 1304. FIG. 13A shows a coaxial design, any form factor or shape, such as a planar sheet, may alternatively be used. Various lactate sensor designs are described in Rathee et al., "Biosensors based on electrochemical lactate detection: A comprehensive review," Biochemistry and Biophysics Reports 5 (2016) pp. 35-54, and Rasaei et al., "Lactate Biosensors: current status and outlook," Analytical and Bioanalytical Chemistry, September 2013, both of which are incorporated herein by reference in their entirety.

[0350] Figure 13B is a cross-sectional view of the electroactive section of the exemplary sensor of Figure 13A showing the exposed electroactive surface of the working electrode surrounded by a sensing membrane in one embodiment. Such sensing membranes are present in a variety of lactate sensor designs. As shown in Figure 13B, the sensing membrane is deposited over at least a portion of the electroactive surfaces of the sensor (working electrode and optionally the reference electrode) to provide protection of the exposed electrode surfaces from the biological environment, diffusion resistance of analytes, catalyst to enable enzymatic reactions, restriction or blocking of interferents, and / or hydrophilicity at the electrochemically reactive surfaces of the sensor interface.

[0351] Thus, the sensing membrane may include multiple domains, such as an electrode domain 1307, an interference domain 1308, an enzyme domain 1309 (e.g., including lactate oxidase), and a resistance domain 1300, and may include a high oxygen solubility domain, and / or a bioprotective domain (not shown). The membrane system is deposited on the exposed electroactive surface using known thin film techniques (e.g., spraying, electrodeposition, dipping, etc.). In one embodiment, one or more domains are deposited by immersing the sensor in a solution and withdrawing the sensor at a rate that provides the appropriate domain thickness. However, the sensing membrane may be arranged over (or deposited on) the electroactive surface using any known method, as will be appreciated by one of skill in the art.

[0352] The sensing membrane generally includes an enzyme domain 1309 arranged such that it is placed more distally from the electroactive surface than the interference domain 1308 or the electrode domain 1307. In some embodiments, the enzyme domain is deposited directly on the electroactive surface. In a preferred embodiment, the enzyme domain 1309 provides an enzyme such as lactose oxidase to catalyze the reaction of the analyte with its co-reactant.

[0353] The sensing membrane may also include a resistive domain 1300 arranged more distally from the electroactive surface than the enzyme domain 1309 since there is a molar excess of lactate relative to the amount of oxygen in the blood. However, enzyme-based sensors that use oxygen as a co-reactant are preferably provided with oxygen in a non-rate-limiting excess in order for the sensor to respond accurately to changes in analyte concentration, rather than having a reaction that cannot utilize the analyte present due to a lack of oxygen co-reactant. This has been found to be a problem with glucose concentration monitors, which is why a resistive domain is included. Specifically, when the glucose monitoring reaction is oxygen-limited, linearity is not achieved above a minimum concentration of glucose. In the absence of a semi-permeable 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 2 or 3 mM. However, in a clinical setting, a linear response to glucose levels is desired up to at least about 20 mM. To allow for accurate determination of higher glucose levels, the resistive domain in a glucose monitoring situation can be 200 times more permeable to oxygen than to glucose. This allows for a sufficiently high oxygen concentration to make the glucose concentration a determining factor in the rate of the detected electrochemical reaction.

[0354] In some embodiments, for lactate sensors described herein, the resistance domain can be thinner and have a smaller difference in analyte to oxygen permeability, such as an oxygen to lactate permeability of 50:1 or 10:1. In some embodiments, this makes the lactate sensor more sensitive to low lactate levels, such as below 0.5 mM to 3 or 4 mM. The resistance domain can be configured such that lactate is the rate limiting reactant at 3 mM lactate or less, thus allowing accurate threshold detection at about 2 mM. The resistance domain can be further configured to allow oxygen to become the rate limiting reactant at lactate concentrations above 10 mM. These ranges can be further narrowed in some embodiments, for example, the resistance domain can be configured such that lactate is the rate limiting reactant at lactate concentrations below 4 mM and oxygen is the rate limiting reactant at lactate concentrations above 6 mM. In this way, the sensor itself can be optimized for early sepsis detection. It will also be appreciated that in addition to lactate, other analyte sensors, such as sensors suitable for ketones, ethanol, glycerol, glucose, hormones, viruses, or any other biological component of interest, can be combined with the lactate sensors described herein.

[0355] 14A-14C illustrate an example implementation of the sensor system 104 implemented as a wearable device, such as an on-skin sensor assembly 1400. As shown in FIGS. 14A and 14B, the on-skin sensor assembly includes a housing 1428. An adhesive patch 1426 can couple the housing 1428 to the skin of a host. The adhesive 1426 can be a pressure sensitive adhesive (e.g., acrylic, rubber, or other suitable type) bonded to a carrier substrate (e.g., spunlace polyester, polyurethane film, or other suitable type) for skin attachment. The housing 1428 can include a through hole 1480 that cooperates with a sensor inserter device (not shown) used to implant the sensor 1338 under the skin of a subject.

[0356] The wearable sensor assembly 1400 includes sensor electronics 1435 operable to measure and / or analyze a lactate concentration indicator sensed by a lactate sensor 1438. As shown in FIG. 14C, in this implementation, the sensor 1338 extends from its distal end into a through-hole 1480 and is routed to the sensor electronics 1435, which is typically mounted on a printed circuit board 1435 inside the housing 1428. The sensor electrodes are connected to the sensor electronics 1435. These types of analyte monitors are currently used in commercially available glucose monitoring systems used by diabetic patients, and the design principles used therein may be used for lactate monitors as well.

[0357] The housing 1428 of the sensor assembly 1400 can include a user interface for delivering messages to the patient regarding the sepsis condition. Because the lactate sensor described herein, in some examples, may not be a monitor worn by the patient on a regular basis, as is the case with glucose monitors, in such examples, it may not be necessary to include many of the features present in other monitor types, such as periodic wireless transmission of analyte concentration data. Thus, a simple user interface may be implemented simply to deliver an alert. In some embodiments, the user interface may be a single light-emitting diode (LED) that is illuminated when the sensor electronics determine that a sepsis risk is present. The two LEDs or bi-color LED may be green when the monitor is operational and detects a low risk, and red when a sepsis risk is detected and an alert is issued. The monitor may be configured to return to a green or low risk state when the measurements return to a value appropriate for its output. To provide additional flexibility in delivering messages to the patient, such as error messages, time remaining to wear the device, etc., simple dot-matrix character displays can still be used that are inexpensive and power efficient (e.g., less than 200 pixels on a side, or a configurable 20 character LCD).

[0358] Additional Considerations The methods disclosed herein include one or more steps or actions for achieving the method. These method steps and / or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or ...

Claims

1. 1. A surveillance 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:

10. The monitoring system of claim 1, comprising an electroactive working electrode of conductive material configured for insertion into the patient's skin, the electroactive working electrode surrounded by a sensing membrane for sensing the analyte level.

3. the continuous analyte sensor is a continuous lactate sensor; The monitoring system of claim 1 , wherein the analyte measurement comprises a lactate measurement.

4. a memory containing executable instructions; and one or more processors in data communication with the sensor electronics module and configured by the executable instructions to: receiving analyte data from the sensor electronics module, the analyte data including the lactate measurement associated with at least a first time period; processing the analyte data from the at least a first time period to determine at least one lactate-derived metric; and 4. The monitoring system of claim 3, configured to generate a disease prediction using the at least one lactate-derived metric.

5. The monitoring system of claim 4 , wherein the processor is further configured to generate one or more recommendations for treatment based at least in part on the disease prediction.

6. The one or more treatment recommendations include: Recommendations for improving lifestyle habits, Drug prescription recommendations, Surgery recommended, or The monitoring system of claim 5 , including at least one of medical device recommendations for use by the patient.

7. The disease prediction comprises: an indication of the presence of liver disease in said patient; an indication of the severity of said liver disease in said patient; a score associated with the liver disease of the patient; an indication of the level of risk that said patient will be diagnosed with said liver disease; an indication of the level of improvement or worsening of said liver disease in said patient; an indication of the level of improvement or worsening of said liver disease in said patient in response to an investigational drug and / or device intervention, said device intervention including a gastric bypass device, an electrical muscle stimulation device, or a TENS device; the patient's risk of death; or The monitoring system of claim 4 , comprising at least one of an identification of one or more diseases associated with the liver disease of the patient and an associated risk of the patient being diagnosed with the one or more diseases.

8. The indicator of the level of improvement or worsening of the liver disease in the patient is: any procedures previously performed on the patient; The monitoring system of claim 7 , based at least in part on at least one of the medications previously taken by the patient.

9. 5. The monitoring system of claim 4, wherein the at least one lactate derived metric comprises at least one of lactate clearance rate, lactate area under the curve, lactate baseline, lactate rate of change, or postprandial lactate level.

10. 5. The monitoring system of claim 4, further comprising one or more non-analyte sensors configured to generate non-analyte sensor data during the first period of time.

11. the at least one lactate-derived metric comprises at least a first lactate clearance rate; The processor being configured to process the analyte data from the at least first time period to determine the at least one lactate clearance rate, the processor comprising: identifying at least one lactate increasing period for said patient during said at least a first period of time; calculating a first lactate clearance rate for the patient after the at least one lactate increasing period; and 11. The monitoring system of claim 10, further comprising: configured to correct the first lactate clearance rate of the patient to isolate lactate clearance by the patient's liver based at least in part on the non-analyte sensor data.

12. The at least one lactate increasing phase may comprise: physical exertion by said patient, or The monitoring system of claim 11 , resulting from at least one of lactate consumption by the patient.

13. The processor being configured to calculate the first lactate clearance rate for the patient after the at least one lactate increasing phase, the processor comprising: determining a maximum lactate level for said patient during said at least one lactate increasing period; determining the amount of time it takes for the maximum lactate level to decrease to a percentage of the patient's baseline lactate level or to a percentage of the maximum lactate level after the at least one lactate increase period; and 12. The monitoring system of claim 11, further configured to calculate the first lactate clearance rate for the patient using the determined maximum lactate level for the patient, the baseline lactate level for the patient, and the determined amount of time it takes for the maximum lactate level to decrease to the percentage of the baseline lactate level for the patient.

14. The processor being configured to correct the first lactate clearance rate for the patient, the processor comprising: using the non-analyte sensor data to identify that the at least one lactate increase period is due to physical exertion by the patient; comparing the non-analyte sensor data to other non-analyte sensor data for one or more other lactate increase periods having predetermined lactate clearance rate profiles that are attributable to physical exertion and that represent a profile of lactate clearance by at least one of the patient's liver, kidneys, muscles, and heart; and determining a second lactate clearance rate indicative of lactate clearance solely by the liver in the patient based at least in part on the comparison; 12. The monitoring system of claim 11, wherein the disease prediction is generated using the analyte data for at least the one or more analytes and the second lactate clearance rate.

15. The processor being configured to correct the first lactate clearance rate for the patient, the processor comprising: using the non-analyte sensor data to identify that the at least one lactate increase period is not attributable to physical exertion by the patient; comparing the data generated by the non-analyte sensor data to other non-analyte sensor data for one or more other lactate increase periods that are not attributable to physical exertion and have a predetermined lactate clearance rate profile representative of lactate clearance by at least one of the patient's liver, kidneys, muscles, and heart; determining a second lactate clearance rate indicative of lactate clearance solely by the liver in the patient based at least in part on the comparison; 12. The monitoring system of claim 11, wherein the disease prediction is generated using the analyte data for at least one or more analytes and the second lactate clearance rate.

16. 5. The monitoring system of claim 4, wherein the disease predictions are generated using a model trained using training data, the training data including historical patient records having various stages of liver disease.

17. The processor, and further configured to obtain at least one of demographic information, food consumption information, activity level information, or medication information regarding the patient; The monitoring system of claim 4 , wherein the disease prediction is generated further using at least one of the demographic information, the food consumption information, the activity level information, or the medication information.

18. The monitoring system of claim 4 , wherein the one or more analytes further comprise at least one of glucose or ketones.

19. The monitoring system of claim 4 , wherein the one or more analytes in the patient are monitored continuously, semi-continuously, or periodically.

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