Systems and methods for optimizing treatment using physiological profiles

Abstract

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

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the priority and benefit of U.S. Provisional Patent Application No. 63 / 365,702, filed on June 1, 2022; U.S. Provisional Patent Application No. 63 / 376,673, filed on September 22, 2022; U.S. Provisional Patent Application No. 63 / 387,078, filed on December 12, 2022; U.S. Provisional Patent Application No. 63 / 377,332, filed on September 27, 2022; U.S. Provisional Patent Application No. 63 / 403,568, filed on September 2, 2022; and U.S. Provisional Patent Application No. 63 / 403,582, filed on September 2, 2022, which are assigned to the assignee of this specification and are hereby expressly incorporated by reference in their entirety for all applicable purposes as if fully set forth herein below.

Background Art

[0002] The kidneys perform many important functions in the human body, including filtering waste products and excess fluids excreted in urine, and removing acids produced by the body's cells to maintain a healthy balance of water, salts, and minerals (such as sodium, calcium, phosphorus, and potassium) in the blood. In other words, the kidneys play a major role in homeostasis through kidney mechanisms that transport water, salts, and minerals and regulate their secretion, reabsorption, and excretion. Further, the kidneys secrete renin (such as angiotensinogenase), which forms part of the renin - angiotensin - aldosterone system (RAAS) that mediates extracellular fluid and arterial vasoconstriction (e.g., blood pressure). More specifically, high blood pressure (e.g., hypertension) can be regulated through RAAS inhibitors such as angiotensin - converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARB). When the kidneys become diseased or damaged, impairment or loss of these functions can cause significant damage to the human body.

[0003] Kidney diseases occur when the kidneys become diseased or damaged. Kidney diseases are generally classified as either acute or chronic based on the duration of the disease. Acute kidney injury (AKI) (also referred to as "acute renal failure") is usually caused by events that lead to kidney dysfunction, such as dehydration, blood loss due to major surgery or injury, and / or the use of medications. On the other hand, chronic kidney disease (CKD) is usually caused by long-term diseases such as hypertension or diabetes that gradually damage the kidneys and reduce their function over time.

[0004] Conventional kidney disease diagnostic methods and systems include albumin / creatinine ratio (ACR) tests, glomerular filtration rate (GFR) tests, and blood tests that monitor a patient's potassium level. CKD is classified into five stages based on the severity of kidney dysfunction measured by various methods and systems. Kidney diseases in stages 1-3a are mild to moderate kidney dysfunctions. Kidney diseases in stages 3b-5 are moderate to severe kidney dysfunctions. End-stage renal disease (ESRD) is total kidney dysfunction or renal failure.

[0005] The GFR method for diagnosing and staging kidney diseases represents the flow rate of the fluid filtered through the kidneys. The creatinine clearance rate is the volume of plasma from which creatinine is removed per unit time and is used to estimate the GFR. The GFR can be measured using the gold standard method (e.g., measured GFR (mGFR)), or it can be estimated using an equation (e.g., eGFR). The eGFR provides a more convenient and rapid analysis for evaluating kidney function.

[0006] In some cases, if CKD is left untreated, when the potassium level in CKD patients increases, it can lead to hyperkalemia, but when the potassium level in CKD patients decreases more, it can lead to hypokalemia. In particular, hyperkalemia is a medical term that describes a blood potassium level higher than normal (e.g., higher than the normal blood potassium level of 3.6 - 5.2 millimoles per liter (mmol / L)). Hyperkalemia increases the risk of cardiac arrhythmia episodes and sudden death. On the other hand, hypokalemia is a medical term that describes a blood potassium level lower than normal. In particular, CKD patients can develop hypokalemia due to gastrointestinal potassium loss from diarrhea or vomiting, or renal potassium loss from non - potassium - sparing diuretics (e.g., diuretics used to increase the amount of fluid passing from the body into the urine regardless of the amount of potassium lost from the body into the urine). Severe hypokalemia and hyperkalemia can lead to severe symptoms of events that cause respiratory failure, sudden cardiac death, or other deaths.

[0007] The kidneys also play an important role in the regulation of blood sugar. The kidneys produce glucose through gluconeogenesis and release glucose into the blood, thereby increasing blood glucose levels. The kidneys also lower blood glucose levels by reabsorbing glucose in the proximal renal tubules. In addition, the kidneys use glucose as an energy source.

[0008] Glucose is a monosaccharide (e.g., a simple sugar). Glucose can not only be ingested but also be produced in the body from proteins and carbohydrates. Serum glucose is maintained at healthy levels through several mechanisms (e.g., glucose homeostasis). High blood glucose (i.e., hyperglycemia) is decreased by insulin and removed by the kidneys. Low blood glucose (i.e., hypoglycemia) is increased by gluconeogenesis in the kidneys and liver.

[0009] An increase in blood glucose levels stimulates insulin release. Insulin causes cells to take up glucose, thereby lowering serum (e.g., extracellular) glucose levels to maintain glucose homeostasis. Insulin also stimulates potassium uptake by cells, thereby lowering serum potassium levels. In some cases, when a patient's glucose level increases and the rate of change of the glucose level in the patient's body is high, excessive insulin may be produced, which can cause excessive intracellular movement of potassium. On the other hand, when a patient's glucose level decreases and the rate of change of the glucose level in the patient's body is low, insulin secretion can be reduced. In certain cases, low insulin can lead to limited access of cells to glucose and potassium, and thus, extracellular glucose and potassium levels can increase. In certain other cases, a diabetic patient may have high insulin levels and high glucose levels, but the high levels of insulin may not be effective in metabolizing glucose due to the patient's potential insulin resistance. On the other hand, high insulin levels in diabetic patients can carry potassium intracellularly and may lower (e.g., regulate) the potassium level in patients receiving insulin.

[0010] Gluconeogenesis is the formation of glucose from precursor molecules (e.g., lactate, glycerol, and / or amino acids). Glucose is formed in the kidney and liver and then released into the circulation. Gluconeogenesis is a mechanism for maintaining glucose homeostasis by preventing low blood glucose (i.e., hypoglycemia). As renal function declines, gluconeogenesis in the kidney also declines, and thus, the kidney's ability to respond to a drop in blood glucose is limited.

[0011] True diabetes is a disorder in which the pancreas cannot make insulin (type I or insulin-dependent), and / or insulin is not effective, or cannot produce a sufficient amount to lower blood glucose to normal levels (type II or non-insulin-dependent). In a diabetic state, the patient is troubled by hyperglycemia, which causes many physiological abnormalities associated with microvascular deterioration (e.g., kidney failure, skin ulcers, or bleeding into the vitreous humor of the eye). Hypoglycemic reactions (i.e., hypoglycemia) can be induced by inadvertent overdosage of insulin, or after normal administration of insulin or glucose-lowering agents, or after inadequate food intake. Treatment of diabetes requires maintenance of glucose homeostasis. Glucose levels can be controlled by various medications, including exogenous insulin.

[0012] In some cases, the patient may suffer from insulin resistance. Insulin resistance occurs when the patient's muscle, fat, and liver cells do not respond adequately to insulin. Thus, glucose metabolism, as well as intracellular potassium movement, can be impaired. As a result, the patient's pancreas makes more insulin to help glucose and insulin enter the patient's cells. Furthermore, the effects of insulin resistance on glucose metabolism can vary among different patients.

[0013] Many drugs can also affect renal function or, if not, be affected by renal function. Some drugs or medical procedures, such as dialysis and diuretics, can replace or supplement renal function. Other drugs or medical procedures, such as nonsteroidal anti-inflammatory drugs (NSAIDs), e.g., ibuprofen (e.g., Advil, Motrin) and naproxen (e.g., Aleve), vancomycin, iodinated radiocontrast agents (e.g., referring to any contrast agent used in diagnostic tests), angiotensin-converting enzyme (ACE), e.g., lisinopril, enalapril, and ramipril, aminoglycoside antibiotics, e.g., neomycdin, gentamicin, tobramycin, and amikacin, anti-viral human immunodeficiency virus (HIV) medications, zoledronic acid (e.g., Zometa, Reclast), foscarnet, etc., can reduce renal function. Further, some medical procedures, such as insulin and statins, can be affected by changes in renal function. Although drugs and / or medical procedures may be known to affect renal function or be affected by renal function, it may be desirable to continue the use of such drugs in some cases.

Brief Description of the Drawings

[0014] To enable a more detailed understanding of the features of the present disclosure listed above, a more specific description, briefly summarized above, can be made by referring to the aspects, some of which are illustrated in the drawings. However, it should be noted that the accompanying drawings merely illustrate certain exemplary aspects of the present disclosure and, therefore, should not be considered as limiting the scope thereof, as the description can be recognized for other equally effective aspects.

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[0015] For ease of understanding, the same reference numbers are used, where possible, to designate the same elements common to each figure. It is contemplated that elements disclosed in one aspect may be beneficially utilized in other aspects without specific recitation.

DETAILED DESCRIPTION OF THE INVENTION

[0016] Many medical treatments are known to affect renal function or to be affected by renal function, but it may be desirable to continue the use of such medical treatments. An improved understanding of how these medical treatments affect renal function and how they are affected by renal function can lead to improved treatment efficacy and reduced harm to the kidneys and other body systems.

[0017] Dialysis is a treatment for kidney failure that sweeps unwanted toxins, waste products, and excess fluid from the body by filtering the patient's blood. Dialysis helps maintain a healthy balance of water, salts, and minerals in the body. Dialysis also helps control blood pressure. Dialysis treatment can be performed by a hemodialysis machine or a peritoneal dialysis machine. During hemodialysis, the blood is filtered through a dialyzer (i.e., a dialysis machine). Blood and dialysate (e.g., dialysis solution) pass through, allowing waste products to leave the blood and move into the dialysate within the dialysis machine for disposal. During peritoneal dialysis, a catheter is placed inside the patient's abdominal cavity, and dialysate is used to filter the blood through the peritoneum. Waste products move from the blood into the dialysate and are ultimately removed and discarded. Peritoneal dialysis is often performed at home, during the night while the patient is sleeping.

[0018] Diuretics are drugs used to treat hypertension by reducing the volume of body fluids, thereby lowering blood pressure. Diuretics function by increasing the amount of fluid that passes from the body into the urine. Diuretics can be non-potassium-sparing or potassium-sparing. Non-potassium-sparing diuretics (e.g., thiazides and loops) function regardless of the amount of potassium lost from the body into the urine. Potassium-sparing diuretics increase the amount of fluid removed from the body but do not lower potassium levels. Diuretics have different pharmacokinetic availabilities for different patients, and each can have a different effect on each patient.

[0019] As shown by diuretics and dialysis, medical treatments can have different pharmacokinetic activities and biological responses that are specific to each patient. The duration of a medical treatment may be one or more periods during which the medical treatment induces a biological response in the user. The biological response may include the activity, absorption, pharmacodynamics, affinity, and / or effectiveness of the medical treatment on the patient.

[0020] Therefore, it is desirable to adjust the medical treatment according to the activity and biological response of a particular patient. Further, the adjustment of the medical treatment may include in-treatment adjustments to improve the activity and biological response.

[0021] Overall, existing methods for determining treatment parameters have a first problem in that they cannot take into account the biological responses and treatment pharmacokinetic activities of individual patients, including the individual patient's renal function. Currently, using existing methods, treatment parameters can either not be adjusted at all with respect to the patient's response or are only gradually adjusted over time based on feedback from the patient and / or healthcare provider (HCP). Further, when using existing methods, treatment parameters may not be determined using patient-specific data.

[0022] Existing methods for determining treatment parameters also have a second problem in that they cannot capture the current and changing health states of individual patients. In particular, existing methods cannot continuously monitor a patient's health by monitoring the concentration of changing analytes such as potassium and / or glucose to indicate the patient's current and evolving state. As used herein, the term "continuous" can mean fully continuous, semi-continuous, periodic, etc. Such continuous monitoring of analytes is advantageous in disease diagnosis and staging because continuous measurements can continuously provide information regarding the tendency and rate of analyte change over a continuous period with respect to the most recent measurement. Using such information, it is possible to predict analyte patterns before, during, and after treatment, determine the likelihood of adverse events during and after treatment, and generate optimal treatment parameters and / or other recommendations for periods before, during, and after treatment.

[0023] As a result of these problems, current medical treatments are not optimized for the health of patients with kidney disease and / or diabetes and are not adjusted to complement the patient's current health status. However, predicting a patient's renal function in relation to a medical treatment, determining the likelihood of adverse events during treatment, and generating optimized treatment parameters can reduce the risk of health-harmful events and prevent the overall deterioration of renal function. Such optimized parameters can account for competing risks and promote the patient's overall health, including reducing the risk of serious medical conditions and even death.

[0024] Accordingly, certain embodiments described herein provide a technical solution to the above technical problems by providing an improved decision support and diagnostic system configured to take into account the impact of a medical treatment on a patient's physiological function (e.g., analyte levels) and the impact of the patient's physiological function on the effectiveness of the medical treatment (e.g., decline in renal function) in order to optimize the treatment of the patient to reduce the risk of health-harmful events. As discussed in more detail herein, the decision support system presented herein is designed to provide optimal treatment parameters for medical treatments that affect and can be affected by a patient's physiological function, as well as other decision support for the management of such medical treatments.

[0025] For example, the decision-making support system described in this specification collects and / or generates data including, for example, analyte data, patient information, and non-analyte sensor data during various periods (e.g., treatment period, pre-treatment period, and / or post-treatment period), and (1) identifies the risk of adverse events during various periods based on the corresponding physiological profiles, (2) provides recommended treatment parameters for the implementation of medical treatments, and / or automatically controls the operation of one or more medical devices (e.g., dialysis devices, insulin pumps, etc.) based on such recommended treatment parameters, and is configured to create various corresponding physiological profiles that can be used to make patient-specific treatment decisions or recommendations to help address the identified risk of adverse events. Additionally or alternatively, the continuous analyte monitoring system can provide decision-making support to a 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 can include continuously monitored glucose data and / or continuously monitored potassium data, in addition to other continuously monitored analyte data such as lactate, insulin, phosphate, bicarbonate, calcium, magnesium, sodium, blood urea nitrogen (BUN), etc., and / or other data related to other analytes mentioned in this specification. The collected data also includes patient information that can include information related to age, gender, kidney disease, family history of kidney disease, other health conditions, etc. The secondary sensor data can include accelerometer data, heart rate data (ECG, HRV, HR, etc.), temperature, blood pressure, sweat sensor, impedance sensor, dialysis device data sensor, or any other sensor data other than analyte data.

[0026] Additionally or alternatively, the decision support system described herein may use various algorithms or artificial intelligence (AI) models, such as machine learning models, trained based on patient-specific data and / or population data to provide real-time decision support to a patient based on the collected information about the patient. For example, certain aspects are directed to algorithms and / or machine learning models designed to provide decision support, including predicting and providing optimal treatment parameters for treatment (e.g., based on history and / or real-time data indicating the impact of treatment on a patient's physiological function), predicting and warning a patient about the likelihood of health adverse events associated with treatment, recommending health-related actions to reduce the likelihood of health adverse events, automatically controlling the operation of a medical device (e.g., a dialysis device) based on the predicted optimal treatment parameters, or any combination thereof. The algorithms and / or machine learning models may be used in combination with one or more continuous analyte sensors, including at least a continuous glucose sensor or a continuous potassium sensor, to provide a real-time diabetes assessment.

[0027] The algorithms and / or machine learning models may consider population data, individualized patient-specific data, or a combination of both when determining the likelihood of an adverse event and providing some of the decision support for medical treatment (e.g., optimized treatment parameters) and other outputs described herein. Additionally or alternatively, the algorithms and / or machine learning models may consider physiological profiles created for a patient based on monitoring the patient over various periods (e.g., treatment period, pre-treatment period, and / or post-treatment period).

[0028] According to certain embodiments, prior to deployment, a machine learning model is trained with training data that includes, for example, user-specific data and / or population data. As described in more detail herein, population data may be provided in the form of a dataset that includes data records of historical patients having various stages of kidney disease, various types of other co-existing diseases, and various medical treatment histories. Each data record is used as an input into the machine learning model to optimize such a model to generate accurate predictions regarding the likelihood of adverse events over various periods of time as well as decision support outputs (e.g., optimal treatment parameters, recommendations, etc.). A combination of a continuous analyte monitoring system with a machine learning model and / or algorithm for (1) predicting the effect of a medical treatment on a patient's physiological function, including predicting the likelihood of adverse events occurring over various periods (e.g., treatment period, pre-treatment period, and / or post-treatment period), and (2) providing decision support for the management of the treatment of kidney disease patients (e.g., predicting optimal treatment parameters, providing recommendations, etc.). For example, the decision support system may be used to improve the effectiveness of medical treatment, reduce the likelihood of adverse events during and after medical treatment (e.g., dialysis), reduce unnecessary medical treatment, prevent new or worsening kidney dysfunction, and / or improve kidney function. The improved medical treatment may, in some cases, reduce the risk of hospitalization, complications, and death.

[0029] Through a combination of a continuous analyte monitoring system and a machine learning model and / or algorithm, the decision support system described herein is configured to provide the accuracy and reliability necessary for a patient's expectations. For example, bias, human error, and emotional influences can be minimized in determining the likelihood of adverse events during various periods (e.g., during or after a treatment period) and / or in generating decision support outputs (e.g., optimal treatment parameters). Additionally, machine learning models and algorithms combined with an analyte monitoring system can provide insights into patterns and / or trends that may previously have been missed and that degrade a patient's health, at least with respect to the kidneys. Accordingly, the decision support system described herein improves existing decision support systems and, more generally, the fields of disease monitoring, diagnosis, and treatment.

[0030] Exemplary decision support system including an exemplary analyte sensor FIG. 1 shows an exemplary decision support system 100 for predicting the effect of a medical treatment on a patient's physiological function, determining the likelihood of adverse events during a treatment period, a pre-treatment period, and / or a post-treatment period, and / or generating optimal treatment parameters and / or other recommendations. The decision support system 100 is configured to provide decision support to a user 102 (individually referred to herein as a user and collectively referred to herein as users) using at least a continuous analyte monitoring system 104 that includes a continuous analyte sensor. Additionally or alternatively, the user may be a patient or, in some cases, a caregiver of the patient. Additionally or alternatively, the decision support system 100 includes a continuous analyte monitoring system 104, a display device 107 that executes an application 106, a decision support engine 114, a user database 110, a medical record database 112, a training system 140, and a decision support engine 114, each of which is described in more detail below.

[0031] As used herein, the term "analyte" is a broad term used in its ordinary meaning that includes, but is not limited to, referring to substances or chemical components in a biological fluid that can be analyzed (e.g., blood, interstitial fluid, cerebrospinal fluid, lymph, or urine). Analytes can include naturally occurring substances, artificial substances, metabolites, and / or reaction products. Analytes measured by the present device and method can include, but are not limited to, potassium, glucose, acarboxyprothrombin; acylcarnitine; adenine phosphoribosyltransferase; adenosine deaminase; albumin; α-fetoprotein; amino acid profile (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); androstenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-β-hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporin A; cystatin C d-penicillamine; deethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylation polymorphism, alcohol dehydrogenase, alpha1-antitrypsin, 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, MCAD, RNA, PKU, Plasmodium vivax, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diphtheria / tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acid / acylglycine; free beta-human chorionic gonadotropin; free erythrocyte protoporphyrin; free thyroxine (FT4); free tri-iodothyronine (FT3); fumarylacetoacetase; galactose / gal-1-phosphate;Galactose-1-phosphate uridyltransferase; Gentamicin; Glucose-6-phosphate dehydrogenase; Glutathione; Glutathione peroxidase; Glycolic acid; Glycosylated hemoglobin; Halofantrine; Hemoglobin variant; Hexosaminidase A; Human erythrocyte carbonic anhydrase I; 17-α-hydroxyprogesterone; Hypoxanthine phosphoribosyltransferase; Immunoreactive trypsin; Lactate; Lead; Lipoprotein (a), B / A-1, β; Lysozyme; Mefloquine; Netilmicin; Phenobarbital; Phenytoin; Phytanic acid / pristanic acid; Progesterone; Prolactin; Prolidase; Purine nucleoside phosphorylase; Quinene; Reverse tri-iodothyronine (rT3); Selenium; Serum pancreatic lipase; Sisomicin; Somatomedin C; Specific antibodies that recognize any one or more of the following (Adenovirus, Antinuclear antibody, Anti-zeta antibody, Arbovirus, Pseudorabies virus, Dengue virus, Medina worm, Taenia solium, Entamoeba histolytica, Enterovirus, Giardia disease, Helicobacter pylori, Hepatitis B virus, Herpes virus, HIV-1, IgE (atopic disease), Influenza virus, Donovan Leishmania, Leptospira, Measles / mumps / rubella, Borrelia, Mycoplasma pneumoniae, Myoglobin, Spirometra mansoni, Parainfluenza virus, Plasmodium, Poliovirus, Pseudomonas aeruginosa, Respiratory syncytial virus, Coronavirus including but not limited to Covid-19, Rickettsia (scrub typhus), Schistosoma mansoni, Toxoplasma, Treponema pallidum, Trypanosoma cruzi / Langeli, 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 that naturally exist in blood or interstitial fluid can also constitute the analyte in certain implementations. An ion is a charged atom or compound that can include, for example, sodium, potassium, calcium, chloride, nitrogen, or bicarbonate. An analyte can naturally exist in biological fluids such as metabolites, hormones, antigens, antibodies, ions, etc. Alternatively, the analyte can be introduced into the body or be exogenous, such as a contrast agent for imaging, a radioisotope, a chemical agent, a fluorocarbon-based synthetic blood, a challenge agent analyte (e.g., introduced for the purpose of measuring an 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; marijuana (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorinated hydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamine, methamphetamine, Ritalin, Cylert, Preludin, Didrex, Prestate, Bolarnyl, Sandrex, Pregan); antidepressants (barbiturates, methaqualone, Valium, Librium, Miltown, Serax, Equanil, Tranxene, etc., which are psychotropic drugs); hallucinogens (fenciclonine, lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tussionex, fentanyl, Darvon, Talwin, Lomotil); synthetic narcotics (fentanyl, meperidine, amphetamine, methamphetamine, and analogs of fenciclonine, such as ecstasy); anabolic steroids;and drugs or pharmaceutical compositions that may include, but are not limited to, nicotine. Metabolites of the drugs and pharmaceutical compositions are also intended analytes. For example, analytes such as ascorbic acid, uric acid, dopamine, norepinephrine, 3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxytryptamine (5HT), and 5-hydroxyindoleacetic acid (5HIAA), as well as intermediates of the citric acid cycle and other chemicals produced in the body, such as neurochemicals, can also be analyzed.;

[0032] Analytes measured and analyzed by the devices and methods described herein include lactate, insulin, phosphate, bicarbonate, calcium, magnesium, sodium, blood urea nitrogen (BUN), but other analytes listed above can also be considered and measured, for example, by analyte monitoring system 104, but are not limited thereto.

[0033] Additionally or alternatively, the continuous analyte monitoring system 104 is configured to continuously measure one or more analytes and transmit the analyte measurements to an electronic medical records (EMR) system (not shown in FIG. 1). The EMR system is a software platform that enables the electronic input, storage, and maintenance of digital medical data. The EMR system is generally used throughout hospitals and / or other care facilities to document clinical information about patients over a long period of time. The EMR system organizes and presents data in a way that, for example, interprets the health status and assists clinicians by providing ongoing care, scheduling, billing, and follow-up. Reports for patient clinical care and / or disease management can also be created using the data contained in the EMR system. Additionally or alternatively, the EMR can communicate (e.g., via a network) with the decision support engine 114 to perform the techniques described herein. In particular, as described herein, the decision support engine 114 can obtain data associated with a user, use the obtained data as input to one or more trained models, and output a prediction. In some cases, the EMR can provide the decision support engine 114 with data used as input to one or more models. Further, in some cases, the decision support engine 114 can, after making a prediction, provide the output prediction to the EMR. In other embodiments, an intermediate system, such as an interface engine, may be used with or without a patient matching algorithm, system, or master patient index to coordinate data between such systems, the analyte monitoring system, the cloud database, and / or the EMR.

[0034] Additionally or alternatively, the continuous analyte monitoring system 104 is configured to continuously measure one or more analytes and transmit the analyte measurements to a display device 107 for use by the application 106. In some embodiments, the continuous analyte monitoring system 104 transmits the analyte measurements to the display device 107 through a wireless connection (e.g., a Bluetooth connection). Additionally or alternatively, 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 executing the application 106. In some embodiments, the continuous analyte monitoring system 104 and / or the analyte sensor application 106 transmit the analyte measurements to one or more other individuals interested in the patient's health (e.g., family members or physicians for the patient's real-time treatment and care). The continuous analyte monitoring system 104 may be described in more detail with respect to FIG. 2.

[0035] The application 106 is a mobile health application configured to receive and analyze analyte measurements from the analyte monitoring system 104. In particular, the application 106 stores information about the user, including the user's analyte measurements, in the user's user profile 118 for use by a decision support engine 114 for processing and analysis and for providing decision support recommendations or guidance to the user.

[0036] The decision-making support engine 114 refers to a set of software instructions having one or more software modules, including a data analysis module (DAM) 116. Additionally or alternatively, the decision-making 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-making support engine 114 via a network (e.g., the Internet). In some other embodiments, the decision-making 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-making support engine 114 executes entirely on one or more local devices such as the display device 107. As will be discussed in more detail herein, the decision-making support engine 114 may provide decision-making support recommendations to the user via the application 106. The decision-making support engine 114 provides decision-making support recommendations based on the information included in the user profile 118.

[0037] User profile 118 may include information collected regarding the user from application 106. For example, application 106 provides a set of inputs 128, including analyte measurements received from continuous analyte monitoring system 104, stored within user profile 118. Additionally or alternatively, inputs 128 provided by application 106 include other data in addition to analyte measurements received from continuous analyte monitoring system 104. For example, application 106 may obtain additional inputs 128 via manual user input, one or more other non-analyte sensors or devices, other applications running on display device 107, etc. Non-analyte sensors or devices include, but are not limited to, an insulin pump, an electrocardiogram (ECG) sensor or heart rate monitor, an acoustic sensor, a blood pressure sensor, a barometric pressure sensor, an atmospheric oxygen sensor, a sweat sensor, a respiratory sensor, a thermometer, a peritoneal dialysis device, a hemodialysis device, sensors or devices provided by display device 107 (e.g., an accelerometer, a camera, a global positioning system (GPS), a heart rate monitor, a thermometer, etc.), or other user accessories (e.g., a smartwatch), or any one or more of any other sensors or devices that provide related information regarding the user. In certain embodiments, the non-analyte sensors may be incorporated into display device 107 or may include separate sensors and / or devices not incorporated into display device 107. Inputs 128 to user profile 118 provided by application 106 are described in further detail below with respect to FIG. 3.

[0038] The DAM 116 of the decision-making support engine 114 is configured to process a set of inputs 128 in order to determine one or more metrics 130. The metrics 130, which will be considered in more detail below with respect to FIG. 3, may in at least some cases indicate the health or condition of the user, such as one or more of the user's physiological state, health, or trends associated with the user's condition. In certain embodiments, the user's physiological state may be based on the user's core body temperature, blood pressure, heart rate, circadian rhythm, etc. Additionally or alternatively, the metrics 130 may then be used by the decision-making support engine 114 as an input for providing guidance to the user. As shown, the metrics 130 are also stored in the user profile 118.

[0039] User profile 118 also includes demographic information 120, disease progression information 122, and / or pharmaceutical information 124. Additionally or alternatively, such information may be provided via user input or obtained from a specific data storage device (e.g., electronic medical record (EMR), etc.). Additionally or alternatively, demographic information 120 may include one or more of the user's age, body mass index (BMI), ethnicity, gender, etc. Additionally or alternatively, disease progression information 122 may include information about the user's diseases, such as whether the user has previously been diagnosed with acute kidney injury (AKI), chronic kidney disease (CKD), and / or diabetes, or whether the user has a medical history of hyperkalemia, hypokalemia, hyperglycemia, hypoglycemia, etc. Additionally or alternatively, information about the user's diseases may also include the length of time since diagnosis, the stage of the disease, the level of disease control, the level of compliance with disease management therapies, the predicted renal function, other types of diagnoses (e.g., heart disease, obesity), or measurements from a health check (e.g., heart rate, blood pressure, exercise, stress, sleep, etc.). Additionally or alternatively, disease progression information 122 may be provided as the output of one or more prediction algorithms and / or trained models based on analyte sensor data generated via, for example, continuous analyte monitoring system 104. Additionally or alternatively, disease progression information 122 may be provided by manual or semi-manual input from a clinical provider. For example, disease progression information 122 may be provided as the output of one or more models, and then the output may be confirmed by a clinical provider.

[0040] Additionally or alternatively, pharmaceutical information 124 may include information about the amount, frequency, and type of drug treatment (e.g., medications and / or health treatments) administered to the user. Additionally or alternatively, the amount, frequency, and type of medical treatments administered to the user are timestamped and correlated with the user's timestamped analyte levels, analyte change rates, adverse events, indicators of renal function, etc., thereby showing the impact that the amount, frequency, and type of medical treatments have on the user's analyte levels, renal function, risk of experiencing adverse events, etc.

[0041] Additionally or alternatively, the pharmaceutical information 124 may include information regarding one or more medical procedures known to be useful in managing renal function. Dialysis, including hemodialysis and / or peritoneal dialysis, etc., may be mentioned as one or more medical procedures to be managed in relation to the management of renal function. As will be described in more detail below, the decision support system 100 may be configured to use the pharmaceutical information 124 to determine the optimal medical treatment parameters to be prescribed for different users. In particular, the decision support system 100 may be configured to identify one or more optimal dialysis treatment parameters based on the patient's health, the patient's current condition, and / or the effectiveness of the dialysis treatment. Additionally or alternatively, the decision support system 100 may be configured to identify, such as prioritizing a user from a group of users for kidney transplantation when the user is a suitable candidate for kidney transplantation. The transplantation information may further include additional prognostic information, for example, the optimal time to initiate kidney transplantation for a particular user.

[0042] Additionally or alternatively, the pharmaceutical information 124 may include information regarding the consumption of one or more drugs known to damage the kidneys. One or more drugs known to damage the kidneys may include nonsteroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen (e.g., Advil, Motrin) and naproxen (e.g., Aleve), vancomycin, iodinated radiocontrast agents (e.g., referring to any contrast agent used in diagnostic tests), angiotensin-converting enzyme (ACE), such as lisinopril, enalapril, and ramipril, aminoglycoside antibiotics, such as neomycdin, gentamicin, tobramycin, and amikacin, anti-viral human immunodeficiency virus (HIV) pharmaceuticals, zoledronic acid (e.g., Zometa, Reclast), foscarnet, etc.

[0043] Additionally or alternatively, the pharmaceutical information 124 may include information regarding the consumption of one or more drugs known to control complications of kidney disease. One or more drugs known to control complications of kidney disease may include medications for lowering blood pressure and maintaining kidney function, such as ACE inhibitors or angiotensin II receptor blockers, medications for treating anemia, such as supplements of the hormone erythropoietin, medications used to lower cholesterol levels, such as statins, medications used to prevent weak bones, such as calcium and vitamin D supplements, phosphate binders, and the like.

[0044] Additionally or alternatively, the pharmaceutical information 124 may include information regarding the intake of one or more drugs or treatments known to control and / or improve glucose homeostasis. Examples of one or more drugs known to control and / or improve glucose homeostasis may include medications for lowering blood glucose levels, such as insulin including rapid-acting and long-acting insulin, and other medications for managing blood sugar, such as metformin.

[0045] Additionally or alternatively, the pharmaceutical information 124 may include information regarding the consumption of one or more drugs or treatments known to cause hypoglycemia and / or hyperglycemia. One or more agents known to cause hypoglycemia may include ACE inhibitors, beta blockers, pentamidine, quinolone antibiotics, and salicylates. Alternatively, one or more agents known to cause hyperglycemia, including increased heart rate and systolic blood pressure, may include fluoroquinolone antibiotics, beta blockers, thiazide and thiazide-like diuretics, second-generation antipsychotics (SGA), corticosteroids, calcineurin inhibitors (CNI), and protease inhibitors.

[0046] Additionally or alternatively, 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, such that the user profile 118 is dynamic. Thus, the information in the user profile 118 stored within the user database 110 provides an up-to-date repository of information related to the user.

[0047] The user database 110 refers to a storage server operating in a public or private cloud in some embodiments. The user database 110 may be implemented as any type of data store, including a relational database, a non-relational database, a key-value data store, a file system including a hierarchical file system. In some exemplary implementations, the user database 110 is distributed. For example, the user database 110 may comprise a plurality of distributed persistent storage devices. Further, the user database 110 may be replicated such that the storage devices are geographically distributed.

[0048] The user database 110 includes user profiles 118 associated with a plurality of users that interact in a manner similar to the application 106 running on the display device 107 of other users. The user profiles stored in the user database 110 are accessible not only to the application 106 but also to the decision support engine 114. The user profiles in the user database 110 may be accessible to the application 106 and the decision support engine 114 via one or more networks (not shown). As described above, the decision support engine 114, and more specifically the DAM 116 of the decision support engine 114, can fetch the input 128 from the user database 110 and then calculate a plurality of metrics 130 that can be stored as application data 126 in the user profile 118.

[0049] Additionally or alternatively, the user profile 118 stored in the user database 110 may also be stored in the medical record database 112. The user profile 118 stored in the medical 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 record database 112 essentially provides all data related to each user of the application 106, and the data is stored according to an associated timestamp. The timestamp associated with each piece of information stored in the medical record database 112 can identify, for example, when the information related to the user was acquired and / or updated.

[0050] Furthermore, the medical record database 112 can maintain time-series data collected about a user over a period of time, including that for users of the continuous analyte monitoring system 104 and the application 106. For example, analyte data for a user who used the continuous analyte monitoring system 104 and the application 106 over a five-year period to manage their health may have time-series analyte data associated with the user maintained over the five-year period.

[0051] Furthermore, additionally or alternatively, the medical record database 112 may include data for one or more patients who are not users of the continuous analyte monitoring system 104 and / or the application 106. In addition, the medical record database 112 may include information (e.g., user profiles) about one or more patients who have been examined by, for example, a health management physician (or other known method) and for whom no previous medical treatment related to kidney function has been prescribed, as well as information (e.g., user profiles) about one or more patients who have been examined by, for example, a health management physician (or other known method) and for whom previous medical treatment related to kidney function has been prescribed. The data stored in the medical record database 112 may be referred to herein as population data.

[0052] The data related to each patient stored in the medical record database 112 may provide time-series data collected over the patient's disease lifespan, and the disease may be a kidney disease. For example, the data may include information related to the patient prior to being diagnosed with a kidney disease, and information related to each stage of the kidney disease that has progressed and / or regressed in the patient, including information associated with the patient over the duration of the disease. The data may additionally or alternatively include information related to other diseases such as hyperkalemia, hypokalemia, hyperglycemia, hypoglycemia, diabetes, hypertension, heart conditions and diseases (e.g., coronary artery disease, peripheral artery disease, arrhythmia diseases and conditions, etc.), or similar diseases coexisting in relation to the kidney disease. Such information may indicate the patient's symptoms, the patient's physiological state, the patient's potassium level, the patient's glucose level, the patient's lactate level, the patient's insulin level, the patient's phosphate level, the patient's bicarbonate level, the patient's calcium level, the patient's magnesium level, the patient's sodium level, the patient's blood urea nitrogen level, the status / condition of one or more of the patient's organs, the patient's habits (e.g., activity level, food consumption, etc.), and the medical treatments prescribed over the duration of the kidney disease. The data may further include information regarding the patient's kidney function, as well as the occurrence of adverse events before, during, and after the effective periods of various treatments.

[0053] In patients who also have diabetes, the data may include information related to the patient prior to being diagnosed with diabetes, and information related to the diabetes that has progressed and / or regressed in the patient, including information associated with the patient over the duration of the disease. The data may additionally or alternatively include information related to other diseases such as kidney disease, hyperglycemia, hypoglycemia, hypertension, heart conditions and diseases, or similar diseases coexisting in relation to diabetes. Such information may indicate, over the duration of the disease, the patient's symptoms, the patient's physiological state, the patient's glucose level, the patient's potassium level, the patient's lactate level, the patient's insulin level, the status / condition of one or more of the patient's organs, the patient's habits (e.g., activity level, food consumption, etc.), the medical treatments prescribed, the medical treatment compliance rate, etc. The data may further include information regarding the patient's diabetes status, as well as the occurrence of adverse events before, during, and after the effective periods of various treatments.

[0054] Although depicted as separate databases for clarity, in some embodiments, the user database 110 and the medical history database 112 may operate as a single database. That is, historical and current data related to users of the continuous analyte monitoring system 104 and the application 106, as well as historical data related to patients who were not previous users of the continuous analyte monitoring system 104 and the application 106, may be stored in a single database. The single database may be a storage server operating in a public or private cloud.

[0055] As described above, the decision support system 100 is configured to provide decision support for the management of medical treatment of patients with kidney disease by predicting the effect of medical treatment on kidney function and using a continuous analyte monitoring system 104 that includes at least one of a continuous glucose sensor and a continuous potassium sensor. For example, the decision support engine 114 collects and / or generates data including, for example, analyte data, patient information, and non-analyte sensor data during various periods (e.g., a treatment period, a pre-treatment period, and / or a post-treatment period) to (1) identify the risk of adverse events during the various periods based on corresponding physiological profiles, (2) provide recommended treatment parameters for the implementation of medical treatment, and / or automatically control the operation of one or more medical devices (e.g., a dialysis device, an insulin pump, etc.) based on such recommended treatment parameters to assist in addressing the identified risk of adverse events, and is configured to create various corresponding physiological profiles that can be used to make patient-specific treatment decisions or recommendations. Further, additionally or alternatively, the metrics of each user recorded over time may be analyzed to provide an indication of improvement or deterioration of the patient's health.

[0056] Additionally or alternatively, the decision support engine 114 can collect information associated with the user within the user profile 118 and use it to perform an analysis to predict the effect of a medical treatment on patient physiological functions and provide one or more recommendations for the management of medical treatments that affect renal function. For example, the decision support engine 114 can perform an analysis on the collected information associated with the user within the user profile 118 to determine the rate of change of an analyte over various periods (e.g., treatment period, pre-treatment period, and / or post-treatment period) and generate a corresponding physiological profile. Additionally or alternatively, based on the generated physiological profile, the decision support engine 114 can determine the likelihood that the user will experience an adverse event during the corresponding period and generate optimal treatment parameters or decision support recommendations that can help reduce that likelihood.

[0057] The user profile 118 can be accessible to the decision support engine 114 via one or more networks (not shown) to perform such an analysis. Additionally or alternatively, the decision support engine 114 is configured to provide real-time and / or non-real-time decision support regarding renal disease to users and / or others, including but not limited to healthcare providers (HCPs), the user's family, the user's caregivers, researchers, and / or other individuals, systems, and / or groups that support learning from care or data.

[0058] Additionally or alternatively, the decision support engine 114 may utilize one or more trained machine learning models to make patient-specific treatment decisions or recommendations to assist in addressing the identified risk of adverse events, including (1) identifying the risk of adverse events during various periods based on corresponding physiological profiles, (2) providing recommended treatment parameters for the administration of medical treatment, and / or automatically controlling the operation of one or more medical devices (e.g., dialysis devices, insulin pumps, etc.) based on such recommended treatment parameters. In the illustrated embodiment of FIG. 1, the decision support engine 114 may utilize a trained machine learning model provided by the training system 140. Although depicted as a separate server for clarity of concept, in some embodiments, the training system 140 and the decision support engine 114 may operate as a single server. That is, the model may be trained and used by a single server, or may be trained by one or more servers and deployed for use on one or more other servers. Additionally or alternatively, the model may be trained on one or more virtual machines (VMs) that are at least partially executed on one or more physical servers in a relational and / or non-relational database format.

[0059] The training system 140 is configured to train a machine learning model using training data, which may include data (e.g., from user profiles) associated with one or more patients (e.g., users or non-users of the continuous analyte monitoring system 104 and / or the application 106) who (1) have no kidney disease and have not been prescribed a medical treatment that affects kidney function, (2) have no kidney disease but have been prescribed a medical treatment that affects kidney function, (3) have a kidney disease but have not been prescribed a drug or medical treatment that affects kidney function, or (4) have a kidney disease and have been prescribed a medical treatment that affects kidney function. The training data may be stored in the medical record database 112 and may be accessible to the training system 140 via one or more networks (not shown) for training the machine learning model. The training data may also, in some cases, include user-specific data for a user over time.

[0060] Training data refers to, for example, a characterized and labeled dataset. For example, the dataset 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 property or characteristic. Generally, the features that best characterize the patterns in the data are selected to create a predictive machine learning model. Data labeling is the process of adding one or more meaningful and useful labels to the data to provide context for learning by the machine learning model.

[0061] As an illustrative example, each relevant feature of a user reflected in the corresponding data record can be a feature used when training a machine learning model. Such features can include the user's demographic features (e.g., age, gender, etc.), the user's physiological features, etc. As the user's physiological features, glucose level, change in glucose level from the first timestamp to the second timestamp (e.g., delta), glucose level over time (e.g., glucose level from two or more subsequent timestamps), glucose clearance rate, change in glucose clearance rate from the first timestamp to one or more subsequent timestamps (e.g., delta), glucose clearance rate over time (e.g., glucose clearance rate from two or more subsequent timestamps, average glucose level over time (e.g., daily, weekly, monthly, etc.) (e.g., average glucose from two or more subsequent timestamps), average glucose level during the treatment period on the second day (e.g., average glucose level in the morning, afternoon, or night) compared to the average glucose level during the treatment period on the first day, average glucose level during the event-specific time range on the second day (e.g., in the morning, before bedtime, during sleep, after exercise, after dialysis) compared to the average glucose level during the event-specific time range on the first day, blood glucose variability (e.g., standard deviation of average glucose), change in blood glucose variability from the first series of timestamps (e.g., blood glucose variability from the first timestamp to one or more subsequent timestamps) to the second series of timestamps (e.g., blood glucose variability from the second timestamp to one or more subsequent timestamps) (e.g., delta), blood glucose variability over time (e.g., blood glucose variability from two or more subsequent timestamps), time in range (TIR) (e.g., glucose level at a threshold, above a threshold, below a threshold, or between thresholds), change in TIR from the first timestamp to the second timestamp (e.g., delta), TIR over time (e.g., TIR from two or more subsequent timestamps), glucose clearance rate, change in glucose clearance rate from the first timestamp to one or more subsequent timestamps (e.g., delta), glucose clearance rate over time (e.g.,The derivative and / or difference of the derivative of a measured linear system of glucose levels at one or more specific time stamps, the derivative and / or difference of the derivative of a determined linear system of glucose clearance rates at a specific time stamp or a specific series of time stamps to determine the rate of change of the slope of an increase or decrease in glucose clearance rate, the rate of change of the slope of an increase or decrease in glucose levels, the difference in the stage or severity of diabetes (e.g., delta) from a first time stamp to a second time stamp, the presence and / or severity of diabetes, the blood and / or kidney glucose clearance rate from two or more subsequent time stamps, insulin level, the change in insulin level (e.g., delta) from a first time stamp to a second time stamp, the insulin level over time (e.g., insulin levels from two or more subsequent time stamps), the derivative and / or difference of the derivative of a measured linear system of insulin levels at one or more specific time stamps to determine the rate of change of the slope of an increase or decrease in insulin level, etc. may be mentioned.

[0062] As other physiological characteristics of the user, the user's potassium level, the change in potassium level (e.g., delta) from the first timestamp to the second timestamp, the potassium level over time (e.g., potassium levels from two or more subsequent timestamps), the potassium clearance rate, the change in potassium clearance rate (e.g., delta) from the first timestamp to one or more subsequent timestamps, the potassium clearance rate over time (e.g., potassium clearance rates from two or more subsequent timestamps), the presence and / or severity of diabetes, the change in the stage or severity of diabetes (e.g., delta) from the first timestamp to the second timestamp, the derivative and / or difference of the derivative of the measured linear system of potassium levels at one or more specific timestamps to determine the rate of change of the slope of the increase or decrease in potassium level, the derivative of the determined linear system of potassium clearance rates at a specific timestamp or a specific series of timestamps to determine the rate of change of the slope of the increase or decrease in potassium clearance rate, and / or the difference of the derivative, insulin level, the change in insulin level (e.g., delta) from the first timestamp to the second timestamp, the insulin level over time (e.g., insulin levels from two or more subsequent timestamps), the derivative and / or difference of the derivative of the measured linear system of insulin levels at one or more specific timestamps to determine the rate of change of the slope of the increase or decrease in insulin level, etc. may be mentioned.

[0063] Furthermore, other features may relate to the patient's renal function, such as the presence and / or severity of renal disease, changes (e.g., delta) in the stage or severity of renal disease from a first timestamp to a second timestamp, renal function, changes (e.g., delta) in renal function from a first timestamp to a second timestamp, renal function over time (e.g., renal function from two or more subsequent timestamps), the rate of change of renal function over time, renal function before, during, and after a medical treatment, changes (e.g., delta) in renal function from a first timestamp to a second timestamp (where the first and second timestamps may each be before, during, or after a medical treatment), renal function over time (e.g., renal function from two or more subsequent timestamps before, during, and after a medical treatment), the rate of change of renal function over time (e.g., based on two or more subsequent timestamps before, during, and after a medical treatment).

[0064] As additional or alternative features, the user's lactate level, the change in lactate level (e.g., delta) from a first timestamp to a second timestamp, the lactate level over time (e.g., lactate levels from two or more subsequent timestamps), the derivative and / or difference of derivatives of a measured linear system of lactate levels at one or more specific timestamps for determining the rate of change of the slope of the increase or decrease in lactate level, phosphate level, the change in phosphate level (e.g., delta) from a first timestamp to a second timestamp, the phosphate level over time (e.g., phosphate levels from two or more subsequent timestamps), the derivative and / or difference of derivatives of a measured linear system of phosphate levels at one or more specific timestamps for determining the rate of change of the slope of the increase or decrease in phosphate level, bicarbonate level, the change in bicarbonate level (e.g., delta) from a first timestamp to a second timestamp, the bicarbonate level over time (e.g., bicarbonate levels from two or more subsequent timestamps), the derivative and / or difference of derivatives of a measured linear system of bicarbonate levels at one or more specific timestamps for determining the rate of change of the slope of the increase or decrease in bicarbonate level, calcium level, the change in calcium level (e.g., delta) from a first timestamp to a second timestamp, the calcium level over time (e.g., calcium levels from two or more subsequent timestamps), the derivative and / or difference of derivatives of a measured linear system of calcium levels at one or more specific timestamps for determining the rate of change of the slope of the increase or decrease in calcium level, magnesium level, the change in magnesium level (e.g., delta) from a first timestamp to a second timestamp, the magnesium level over time (e.g., magnesium levels from two or more subsequent timestamps), the derivative and / or difference of derivatives of a measured linear system of magnesium levels at one or more specific timestamps for determining the rate of change of the slope of the increase or decrease in magnesium level, sodium level, the change in sodium level (e.g., delta) from a first timestamp to a second timestamp,The measured derivative and / or difference of derivatives of a linear system of sodium levels at one or more specific timestamps for determining the rate of change of the sodium level over time (e.g., sodium levels from two or more subsequent timestamps), the rate of change of the slope of an increase or decrease in the sodium level, the blood urea nitrogen level, the change (e.g., delta) in the blood urea nitrogen level from a first timestamp to a second timestamp, the blood urea nitrogen level over time (e.g., blood urea nitrogen levels from two or more subsequent timestamps), the measured derivative and / or difference of derivatives of a linear system of blood urea nitrogen levels at one or more specific timestamps for determining the rate of change of the slope of an increase or decrease in the blood urea nitrogen level, the blood pH level, the change (e.g., delta) in the blood pH level from a first timestamp to a second timestamp, the blood pH level over time (e.g., blood urea nitrogen levels from two or more subsequent timestamps), the measured derivative and / or difference of derivatives of a linear system of blood pH levels at one or more specific timestamps for determining the rate of change of the slope of an increase or decrease in the blood pH level, may be mentioned.

[0065] Additionally or alternatively, as other features, non-analyte data, changes (e.g., deltas) in non-analyte data from a first timestamp to a second timestamp, non-analyte data over time (e.g., non-analyte data from two or more subsequent timestamps), the measured derivative and / or difference in derivatives of a linear system of non-analyte data at one or more specific timestamps for determining the rate of change of the slope of an increase or decrease in non-analyte data, and the like may be mentioned. Additionally or alternatively, the non-analyte data may include ECG data that is used for and / or correlated with potassium measurements. The ECG data may be used with or without other combinations of inputs, such as the trend of glucose or the trend of a potassium sensor. The ECG shows the body's physiological response to the most important potassium levels, and in some patients, it is enhanced to withstand higher or lower levels than normal without changing the ECG. Furthermore, extracellular potassium concentration directly affects myocardial depolarization and repolarization. Therefore, it is advantageous to monitor for abnormalities in the ECG signal, such as tall T waves, prolonged PR intervals, decreased P waves, and / or widened QRS complexes. In scenarios where the potassium level is higher or lower than normal and these abnormalities are monitored and detected, warnings can be escalated for corrective measures and / or medical intervention.

[0066] Additionally or alternatively, the dataset may include features associated with the patient's medical treatment and / or the impact of the medical treatment on the patient's physiological functions during and / or after the medical treatment. For example, such features may include medications, medical treatments, and / or health treatments, medical treatment parameters such as type, dosage, timing, frequency, composition, concentration, flow rate, volume, and / or other treatment parameters including dialysis treatment parameters (e.g., hemolysis and / or peritoneal dialysis parameters), one or more other medications and / or treatments administered to the user such as medications for managing blood sugar, one or more drugs known to damage the kidneys, one or more drugs known to control complications of kidney disease prescribed to the user, and / or may be related to one or more medications for treating one or more symptoms of kidney disease, hyperkalemia, hypokalemia, diabetes, and / or other conditions and diseases the user may have. All of the medical treatment parameter features discussed above may be timestamped such that a correlation can be derived between the impact of such parameters on the patient's physiological functions before, during, and / or after the corresponding medical treatment.

[0067] Additionally or alternatively, each data record in the dataset may be labeled with at least one of an indicator of the likelihood that the patient will experience an adverse event before, during, and / or after the treatment period of the medical treatment, one or more treatment parameters for the medical treatment (e.g., dialysis), improvement or deterioration of kidney function during and / or after the medical treatment, and the impact of the change in the treatment on the patient's physiological functions.

[0068] Next, the model is trained by the training system 140 using the characterized and labeled training data. In particular, the features of each data record can be used as input to the machine learning model, and the generated output can be compared to the label associated with the corresponding data record. The model can 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 repeatedly processing each data record corresponding to each patient medical record, additionally or alternatively, the model can predict accurately regarding the patient's physiological functions (e.g., kidney function, analyte levels, analyte change rates, analyte clearance rates, etc.), the risk of health adverse events before, during, and / or after the treatment period of a medical treatment, the optimal treatment parameters for a medical treatment (e.g., to reduce the risk of health adverse events), the improvement or deterioration of kidney function during and / or after a medical treatment, etc. The model can be iteratively refined to generate accurate predictions related to the effects of medical treatments. Further, in certain other embodiments, by repeatedly processing each data record corresponding to each patient medical record, additionally or alternatively, the model can be iteratively refined to generate more accurate predictions.

[0069] As illustrated in FIG. 1, the training system 140 deploys these trained models to the decision support engine 114 for use during runtime. For example, the decision support engine 114 can obtain a user profile 118 associated with a user, use the information within the user profile 118 as input to the trained models, and output a prediction. The prediction can indicate a harmful risk associated with a medical treatment (e.g., shown as output 144 in FIG. 1). The output 144 generated by the decision support engine 114 can also provide patient-specific treatment recommendations to reduce the likelihood of harmful events. For example, the treatment recommendations may provide the optimal treatment parameters to be administered. Providing the optimal treatment parameters may also include automatically controlling the operation of one or more medical devices (e.g., dialysis devices, insulin pumps, etc.) based on such optimal treatment parameters. Additionally or alternatively, the treatment recommendations may include optimal dosages, prioritization of kidney transplants, and / or additional tests to confirm appropriate treatment recommendations. The output 144 can be provided to the user (e.g., through application 106), to the user's medical treatment device for automatically executing the output 114, to the user's caregiver (e.g., 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 for the purpose of improving the user's health, such as by achieving the recommended treatment.

[0070] Additionally or alternatively, the user's own data is used to personalize one or more models that were initially trained based on population data. For example, a model (e.g., trained using population data) can be deployed for use by the decision support engine 114 to predict the user's analyte trends and the risk of adverse health events associated with current medical treatments. After making a prediction using the model, the decision support engine 114 may be configured to obtain the user's actual physiological data (e.g., analyte levels, rate of change, analyte clearance rate, adverse events, etc.) and calculate the loss between the prediction and the actual analyte data, which can be used to retrain the model. Thus, the model can be continuously retrained using the calculated loss between the prediction and the actual physiological data to personalize the model for the user. In another example, a model (e.g., trained using population data) can be deployed for use by the decision support engine 114 to predict in real-time the impact of changes in treatment (e.g., treatment parameters such as type, timing, dosage, etc.) for a particular user experiencing an adverse health event. After making a prediction using the model, the decision support engine 114 may be configured to obtain the actual occurrence, timing, and severity of the user's adverse event and calculate the loss between the prediction and the actual occurrence, timing, and severity of the adverse event, which can be used to retrain the model. Thus, the model can be continuously retrained using the calculated loss between the prediction and the actual physiological data as an input to the model to personalize the model for the user. In another example, personalization of one or more models includes selecting a subset of the population data from subjects having characteristics similar to the user's characteristics (e.g., demographic information, disease progression information, pharmaceutical information). A model trained using the subset of the population data can be deployed for use by the decision support engine 114 to predict the user's analyte trends and the risk of adverse health events associated with current medical treatments.

[0071] Additionally or alternatively, the output 144 generated by the decision support engine 114 may be stored in the user profile 118. Additionally or alternatively, the output 144 may be a prediction regarding the effect of a medical treatment on a patient's physiological function (e.g., renal function, analyte level, analyte rate of change, analyte clearance rate, etc.). Additionally or alternatively, the output 144 may be a patient-specific determination or recommendation regarding medical treatment parameters for optimizing a medical treatment. Additionally or alternatively, the output 144 may be a prediction regarding analyte levels before, during, and / or after a treatment period of a drug. Additionally or alternatively, the output 144 may be a prediction regarding a user's renal function during a treatment period of a drug. Additionally or alternatively, the output 144 may be a prediction of a user's risk for, e.g., adverse health events (e.g., hypoglycemia, hyperglycemia, hypokalemia, hyperkalemia, etc.) caused by a medical treatment. Additionally or alternatively, the output 144 may be patient-specific optimized medical treatment parameters (e.g., dialysis treatment parameters). The output 144 stored in the user profile 118 may be continuously updated by the decision support engine 114. Thus, the predictions and recommendations initially stored as the output 144 in the user profile 118 within the user database 110 and then passed to the medical record database 112 may provide an indicator of the progression of renal disease associated with medical treatment over time, as well as an indicator of the effectiveness of different medical treatment parameters associated with a reduced risk of adverse health events.

[0072] Additionally or alternatively, the model can be trained to provide lifestyle recommendations, exercise recommendations, diet recommendations, medical treatment recommendations, medical intervention recommendations, and other types of decision-making support recommendations to assist the user in managing their treatment based on the user's historical data, including how different treatment parameters, medications, foods, and exercise have affected the user's physiological functions in the past. Additionally or alternatively, real-time access to local food and menu databases can notify recommendations on whether to include or avoid specific menu items based on current glucose and potassium or other analyte information. Additionally or alternatively, the model can be trained to detect the causes underlying specific improvements or deteriorations in a patient's physiological functions (e.g., the occurrence of adverse events). For example, the application 106 can display a user interface having a graph showing the patient's analyte data or its scores along with a trend line, and can show the causes of adverse events (e.g., different treatment parameters, food consumption, exercise, other medical treatments, decreased kidney function, etc.), for example retrospectively.

[0073] FIG. 2 is a diagram 200 conceptually illustrating an exemplary continuous analyte monitoring system 104 including an exemplary continuous analyte sensor having sensor electronics, according to a particular aspect of the present disclosure. For example, the system 104 can be configured to continuously monitor one or more analytes of a user, according to a particular aspect of the present disclosure.

[0074] In the illustrated embodiment, the continuous analyte monitoring system 104 includes a sensor electronics module 204 and one or more continuous analyte sensors 202 associated with the sensor electronics module 204 (referred to herein individually as the continuous analyte sensor 202 and collectively as the continuous analyte sensors 202). The sensor electronics module 204 can wirelessly communicate (e.g., directly or indirectly) with one or more of the display devices 210, 220, 230, and 240. Additionally or alternatively, the sensor electronics module 204 can also wirelessly communicate (e.g., directly or indirectly) with one or more medical devices, such as medical device 208 (referred to herein individually as the medical device 208 and collectively as the medical devices 208), and / or one or more other non-analyte sensors 206 (referred to herein individually as the non-analyte sensor 206 and collectively as the non-analyte sensors 206).

[0075] Additionally or alternatively, the continuous analyte sensor 202 can include a sensor for detecting and / or measuring an analyte. The continuous analyte sensor 202 can 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 device, a subcutaneous device, a transdermal device, a transepidermal device, and / or an intravascular device. Additionally or alternatively, the continuous analyte sensor 202 can be configured to continuously measure a user's analyte level using one or more measurement techniques, such as enzyme, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, electrophoretic, radiometric, immunochemical. In certain embodiments, the continuous analyte sensor 202 provides a data stream indicative of the concentration of one or more analytes within the user. The data stream can include raw data signals that are then converted into a calibrated and / or filtered data stream used to provide an estimated analyte value to the user.

[0076] Additionally or alternatively, the continuous analyte sensor 202 may be a multi-analyte sensor configured to continuously measure multiple analytes within the user's body. For example, additionally or alternatively, the continuous multi-analyte sensor 202 may be a single multi-analyte sensor configured to measure potassium and glucose within the user's body.

[0077] Additionally or alternatively, one or more multi-analyte sensors may be used in combination with one or more single-analyte sensors. As an illustrative example, the multi-analyte sensor may be configured to continuously measure potassium and glucose and, in some cases, may be used in combination with an analyte sensor configured to measure only lactate levels. Information from each of the multi-analyte sensor and the single-analyte sensor can be combined to provide decision support using the methods described herein.

[0078] Additionally or alternatively, the sensor electronics module 204 includes electronic circuitry associated with the measurement and processing of continuous analyte sensor data, including predictive algorithms associated with the processing and calibration of sensor data. The sensor electronics module 204 can be physically connected to the continuous analyte sensor 202, integrated (non-removably attached) with the continuous analyte sensor 202, or removably attached. The sensor electronics module 204 can include hardware, firmware, and / or software that enables the measurement at the display device level via the continuous analyte sensor 202. For example, the sensor electronics module 204 can include a potentiostat, a power source for powering 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 fixed to, for example, a printed circuit board (PCB) and can take various 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.

[0079] The display devices 210, 220, 230, and / or 240 are configured to display displayable sensor data, including analyte data, that may be transmitted by the sensor electronics module 204. Each of the display devices 210, 220, 230, or 240 can include a display, such as a touch screen display 212, 222, 232, and / or 242, for displaying sensor data to the user and / or receiving input from the user. For example, a graphical user interface (GUI) can be presented to the user for such purposes. In some embodiments, the display device can include another type of user interface, such as an audio user interface, instead of or in addition to the touch screen display, for communicating sensor data to the user of the display device and / or receiving user input. The display devices 210, 220, 230, and 240 can be examples of the display device 107 illustrated in FIG. 1 that are used to display sensor data to the user of FIG. 1 and / or receive input from the user.

[0080] In some embodiments, one, some, or all of the display devices are configured to display or otherwise communicate the sensor data when the sensor data is communicated from the sensor electronics module (e.g., in the data packages transmitted to each respective display device) without any additional expected processing that may be required for calibration and real-time display of the sensor data.

[0081] The plurality of display devices may include custom display devices specially designed to display a particular type of displayable sensor data associated with the analyte data received from the sensor electronics module. Additionally or alternatively, the plurality of 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 plurality of display devices is a smartphone, such as display device 220 representing a mobile phone, configured to use a commercially available operating system (OS) to display a graphical representation of continuous sensor data (e.g., including current and historical data). Other display devices can include a display device 230 representing a tablet, a display device 240 representing a smartwatch, a medical device 208 (e.g., a peritoneal dialysis device or a hemodialysis device), and / or a desktop or laptop computer (not shown).

[0082] Because the user interface varies depending on the display device, the content of the data package (e.g., the amount, format, and / or type of data to be displayed, alarms, etc.) can be customized for each specific display device (e.g., programmed to vary by manufacturer and / or end user). Thus, additionally or alternatively, multiple different display devices can communicate directly wirelessly with a sensor electronics module (e.g., an on-skin sensor electronics module 204 physically connected to a continuous analyte sensor 202, etc.) during a sensor session to enable multiple different types and / or levels of display and / or functionality associated with the displayable sensor data. Additionally or alternatively, the type of alarm customized for each specific display device, the number of alarms customized for each specific display device, the timing of the alarms customized for each specific display device, and / or the threshold levels configured (e.g., for triggering) for each of the alarms are based on the output 144 stored in the user profile 118 for each user (e.g., as noted, the output 144 can indicate the user's current health, the user's glucose and / or potassium status, and / or the current treatment recommended for the user).

[0083] As noted, sensor electronics module 204 can communicate with medical device 208. Medical device 208 can be a passive device in some exemplary embodiments of the present disclosure. For example, medical device 208 can be a dialysis device (e.g., peritoneal dialysis device or hemodialysis device) for filtering a user's blood. For various reasons, it may be desirable for such a dialysis device to receive and track potassium, glucose, phosphate, bicarbonate, calcium, magnesium, sodium, albumin, creatinine, cystatin C, and / or blood urea nitrogen transmitted from continuous analyte monitoring system 104, and continuous analyte sensor 202 is configured to measure potassium, glucose, phosphate, bicarbonate, calcium, magnesium, sodium, and / or blood urea nitrogen. In another example, medical device 208 can 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 values of potassium, glucose, and insulin from continuous analyte monitoring system 104, and continuous analyte sensor 202 is configured to measure potassium, glucose, and / or insulin.

[0084] Further, as noted, sensor electronics module 204 can also communicate with other non-analyte sensors 206. Non-analyte sensors 206 can include, but are not limited to, an altimeter sensor, an accelerometer sensor, a temperature sensor, a respiratory rate sensor, a sweat sensor, etc. Non-analyte sensors 206 can also include monitors such as a heart rate monitor, an ECG monitor, a blood pressure monitor, an impedance sensor, a pulse oximeter, a calorie intake monitor, and a drug delivery device. One or more of these non-analyte sensors 206 can provide data to decision support engine 114, which is further described below. In some aspects, a user can manually provide a portion of the data for processing by training system 140 of FIG. 1 and / or decision support engine 114.

[0085] Additionally or alternatively, the non-analyte sensor 206 can be combined in any other configuration, such as, for example, in combination with one or more continuous analyte sensors 202. As an illustrative example, a non-analyte sensor, such as a heart rate sensor, can be combined with a continuous analyte sensor 202 configured to measure potassium to form a potassium / heart rate sensor 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 heart rate sensor, can be combined with a multi-analyte sensor 202 configured to measure potassium and glucose to form a potassium / glucose / heart rate sensor used to transmit sensor data to the sensor electronics module 204 using a common communication circuit.

[0086] Additionally or alternatively, a wireless access point (WAP) can be used to couple one or more of the continuous analyte monitoring system 104, the plurality of display devices, the medical device 208, and / or the non-analyte sensor 206 to each other. For example, the WAP 138 can provide Wi-Fi and / or cellular connectivity among these devices. Near Field Communication (NFC) and / or Bluetooth can also be used among the devices depicted in FIG. 200 of FIG. 2.

[0087] FIG. 3 illustrates exemplary inputs for use by the decision support system of FIG. 1 and exemplary metrics calculated based on the inputs, according to some embodiments disclosed herein. In particular, FIG. 3 provides a more detailed illustration of the exemplary inputs and exemplary metrics introduced in FIG. 1.

[0088] Figure 3 illustrates an exemplary input 128 on the left, a decision support engine 114 including an application 106 and a DAM 116 in the center, and an alert 130 on the right. Additionally or alternatively, each of the metrics 130 may correspond to one or more values, such as discrete numerical values, ranges, or qualitative values (high / medium / low, stable / unstable, etc.). The application 106 obtains the input 128 via one or more channels (e.g., manual user input, sensors / monitors, other applications running on the display device 107, EMR, etc.). As described above, additionally or alternatively, the input 128 may be processed by the DAM 116 and / or the decision support engine 114 to output the metric 130. The input and the metric 130 may be used by the decision support engine 114 to provide decision support to the user. For example, the input 128 and the metric 130 may be used by a training system 140 to train and deploy one or more machine learning models used by the decision support engine 114 to provide the above-described decision support output.

[0089] Additionally or alternatively, starting from the input 128, the food consumption information may include information about one or more of meals, snacks, and / or beverages, such as one or more of amount, content (milligrams (mg) of potassium, glucose, lactate, sodium, carbohydrates, fat, protein, etc.), order of consumption, and time of consumption. Additionally or alternatively, the food consumption may be provided by the user by providing a photograph through an application configured to recognize the type and amount of food through manual input, and / or by scanning a barcode or menu. In various embodiments, the meal amount may be manually entered as one or more of calories, amount (e.g., "3 cookies"), menu item (e.g., "Royale with Cheese"), and / or food exchange (e.g., 1 fruit, 1 dairy product). In some embodiments, the meal information may be received via a convenient user interface provided by the application 106.

[0090] Additionally or alternatively, the food consumption information may be related to the glucose consumed by the user. Glucose for consumption may include any natural or artificial food or beverage containing glucose, dextrose, or carbohydrates (such as glucose tablets, bananas, or bread). Additionally or alternatively, the food consumption information may be related to the potassium consumed by the user. Potassium for consumption may include any natural or engineered food or beverage containing potassium, such as potassium tablets, electrolyte beverages containing potassium, or bananas.

[0091] Additionally or alternatively, the exercise information is also provided as input. The exercise information can be any information surrounding an activity, such as an activity that requires physical movement by the user. For example, the exercise information can extend to information related to low-intensity (e.g., taking a few steps) and high-intensity (e.g., running 5 miles) physical movements. Additionally or alternatively, the exercise information can be provided, for example, by an accelerometer sensor on a wearable device such as a watch, fitness tracker, and / or patch. Additionally or alternatively, the exercise information can also be provided through manual user input and / or through a surrogate sensor and prediction algorithm that measures changes in heart rate (or other cardiac metrics). When the user predicts that they are exercising based on their sensor data, the user may be asked to confirm whether exercise is taking place, what type of exercise it is, and / or the level of intensity of the strenuous exercise being used during the exercise over a particular period. This data can be used to train system 100 to learn about the user's exercise patterns in order to reduce the need for confirmation questions over time.

[0092] Additionally or alternatively, 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. Additionally or alternatively, user statistics may be provided by interfacing with electronic information sources such as electronic medical records through a user interface and / or from a measurement device. Additionally or alternatively, the measurement device may include one or more of, for example, a wireless, e.g., Bluetooth-enabled, scale, and / or a camera that may communicate with a display device 107 to provide user data.

[0093] Additionally or alternatively, medical treatment information is also provided as input. Medical treatment information may include information about medications, medical treatments, and / or health therapies. Medical treatment information may include the type, dosage, timing, frequency, and / or other such treatment parameters (e.g., composition (e.g., dialysate composition), concentration, flow rate, volume, and / or dialysis treatment parameters, etc.) of one or more medications and / or treatments administered to the user. As referred to herein, medical treatment information may include information about one or more medications for managing one or more blood sugars, one or more drugs known to damage the kidneys, one or more drugs known to control complications of kidney disease prescribed to the user, and / or one or more medications for treating one or more symptoms of kidney disease, hyperkalemia, hypokalemia, diabetes, and / or other conditions and diseases the user may have. Medical treatment information may include information regarding the treatment of kidney disease, including dialysis (e.g., hemodialysis and / or peritoneal dialysis). Medical treatment information may also include information regarding different lifestyle habits, medical treatments, surgical procedures, and / or other non-invasive procedures recommended by the user's physician. For example, the user's physician may recommend to the user an increase / decrease in potassium intake, exercise for at least 30 minutes a day, a change in medication, and / or a change in treatment to maintain and / or improve kidney health, glucose homeostasis, general health, etc. Additionally or alternatively, medical treatment information may be provided by manual input of the user.

[0094] Additionally or alternatively, analyte sensor data may also be provided as input, e.g., through the continuous analyte monitoring system 104. Additionally or alternatively, the analyte sensor data may include glucose data (e.g., the user's glucose value) measured by at least a continuous glucose sensor (or a multi-analyte sensor configured to measure at least glucose) that is part of the continuous analyte monitoring system 104. Additionally or alternatively, the analyte sensor data may include potassium data measured by at least a potassium sensor (or a multi-analyte sensor configured to measure at least potassium) that is part of the continuous analyte monitoring system 104. Additionally or alternatively, the analyte sensor data may include lactate data measured by at least a lactate sensor (or a multi-analyte sensor configured to measure at least lactate) that is part of the continuous analyte monitoring system 104. Additionally or alternatively, the analyte sensor data may include insulin data measured by at least an insulin sensor (or a multi-analyte sensor configured to measure at least insulin) that is part of the continuous analyte monitoring system 104. Additionally or alternatively, the analyte sensor data may include pyruvate data measured by at least a pyruvate sensor (or a multi-analyte sensor configured to measure at least pyruvate) that is part of the continuous analyte monitoring system 104. Additionally or alternatively, the analyte sensor data may include ketone data measured by at least a ketone sensor (or a multi-analyte sensor configured to measure at least ketones) that may be part of the continuous analyte monitoring system 104.

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

[0096] Additionally or alternatively, the input received from the non-analyte sensor may include input related to the user's insulin delivery. In particular, input related to the user's insulin delivery may be received via a wireless connection on a smart pen, via user input, and / or from an insulin pump. The insulin delivery information may include one or more of the volume of insulin, delivery time, etc. Other parameters such as insulin action time, insulin activity rate, or duration of insulin action may also be received as input.

[0097] Additionally or alternatively, the input received from the non-analyte sensor may include input related to the user's dialysis treatment. In particular, input related to the user's dialysis treatment may be received via a wireless connection on the dialysis device, via user input, and / or from the dialysis device. The dialysis device information may include one or more of the dialysate concentration, volume, delivery time, flow rate, cycle, membrane type, device type, etc.

[0098] Additionally or alternatively, the time may also be provided as an input, such as the time of day or the time from a real-time clock. For example, additionally or alternatively, the input analyte data may be timestamped to indicate the date and time when the analyte measurement was made for the user.

[0099] Any user input of the input 128 mentioned above can be through a user interface such as the user interface of the display device 107 in FIG. 1. The user input may also include user symptom data such as stinging pain, nausea, dizziness, fainting, muscle weakness, or palpitations. Such symptom data can be signs of electrolyte balance disorders, can help correlate analyte parameters with symptoms, and can be used to train algorithms / ML models.

[0100] As described above, additionally or alternatively, the DAM 116 and / or the decision support engine (e.g., using one or more trained models) determines or calculates the user's metric 130 based on the input 128. An exemplary list of metrics 130 is shown in FIG. 3.

[0101] Additionally or alternatively, the analyte level can be determined from sensor data (e.g., analyte measurements obtained from the continuous analyte sensor 202 of the continuous analyte monitoring system 104). For example, the analyte level refers to a timestamped analyte level or value that is continuously generated and stored over time. Additionally or alternatively, the analyte level may be a glucose level determined from a continuous glucose sensor. Additionally or alternatively, the analyte level may be a potassium level determined from a continuous potassium sensor. Additionally or alternatively, the analyte level may be one or more of lactate, insulin, phosphate, bicarbonate, calcium, magnesium, sodium, and / or blood urea nitrogen.

[0102] Additionally or alternatively, the analyte baseline can be determined from sensor data (e.g., analyte measurements obtained from the continuous analyte sensor 202 of the continuous analyte monitoring system 104). Additionally or alternatively, the analyte baseline can be determined for one or more analytes including potassium, glucose, lactate, insulin, phosphate, bicarbonate, calcium, and magnesium, sodium, albumin, creatinine, and / or blood urea nitrogen. The analyte baseline represents the user's normal analyte level during a period when significant variation in the analyte level is not typically expected. For example, a user's potassium is generally expected to remain constant over time unless challenged through an act such as the consumption of potassium or potassium-rich foods, or unless it changes as a result of kidney health or decline in kidney function.

[0103] Furthermore, a user can have a different baseline for a particular analyte compared to other users. For example, each user can have a different potassium baseline. Additionally or alternatively, the user's analyte baseline can be determined by calculating the average of the user's analyte levels over a particular amount of time where significant variation is not expected (e.g., where there are no external conditions that would affect the analyte baseline). Additionally or alternatively, the DAM 116 can continuously calculate the analyte baseline (e.g., potassium baseline, glucose baseline, etc.), timestamp the calculated analyte baseline, and store the corresponding information in the user's profile 118.

[0104] In certain other embodiments, to calculate the analyte baseline, the DAM 116 can use analyte levels measured over a period of time, and the user is involved in external events, states, or activities (such as dialysis treatment, drug administration, exercise, food consumption, etc.) that affect the analyte baseline for at least a subset of that period. In such embodiments, the DAM 116, in some examples, first identifies which analyte levels were affected by an event, state, or activity, thereby identifying which measured analyte levels are not to be used to calculate the analyte baseline, and then can exclude such measurements when calculating the user's analyte baseline. In other examples, the DAM 116 can identify which measured analyte levels were affected by an external event, state, or activity and then calculate the baseline using only the analyte levels affected by the external event. The baseline can then be associated with and stored for those external events, states, or activities. For example, a potassium dialysis baseline can be calculated by first identifying which measured potassium levels were affected by the dialysis treatment (e.g., potassium levels measured during the effective period of the dialysis treatment) and then calculating the baseline using only those potassium levels. The effective period of the dialysis treatment may include, for example, the treatment period during which the user performs dialysis using a dialysis device and / or the post-treatment period during which the user continues to be affected by dialysis after stopping the dialysis treatment.

[0105] Additionally or alternatively, whether an analyte level threshold has been reached is determined based on sensor data (e.g., analyte levels obtained from the continuous analyte sensor 202 of the continuous analyte monitoring system 104), health / illness metrics (e.g., described in more detail below), disease stage metrics (e.g., described in more detail below), and / or medical treatment parameters (e.g., described in more detail below). Additionally or alternatively, the analyte threshold level can be determined for one or more analytes including potassium, glucose, lactate, insulin, phosphate, bicarbonate, calcium, magnesium, sodium, creatinine, albumin, and / or blood urea nitrogen. Additionally or alternatively, the threshold level may be consistent for all users. Additionally or alternatively, the threshold level may be input by the end user. Additionally or alternatively, the threshold level may be an absolute maximum or absolute minimum analyte level. Additionally or alternatively, the threshold level may change over time and / or may be adjusted based on sensor data, disease stage, comorbidities, medical treatment, and / or user input. For example, the threshold level may be different during periods when the user is involved in external conditions that affect the analyte level (e.g., dialysis, exercise, food consumption, drug administration).

[0106] In some cases, an external event, condition, or activity that affects a patient's analyte level may be the location of the analyte sensor relative to the drug administration site. For example, the analyte sensor may be worn on the user's body in proximity to a dialysis port (e.g., a peritoneal dialysis port). Additionally or alternatively, the patient's analyte level may be adjusted based on a known or determined distance to the drug administration site. For example, a glucose sensor located near a peritoneal dialysis port may have glucose levels that are artificially increased during dialysis treatment, and thus, the glucose level during dialysis treatment may be adjusted to account for the increased glucose level. In another example where the glucose sensor is placed near a peritoneal dialysis port, glucose measurements during dialysis treatment may be excluded. In some embodiments, the decision support engine 114 may alert the user of external conditions that affect the analyte data and recommend that the user relocate the analyte sensor.

[0107] Additionally or alternatively, the rate of change of the analyte level may be determined from sensor data (e.g., analyte measurements obtained from the continuous analyte sensor 202 of the continuous analyte monitoring system 104). Additionally or alternatively, the rate of change of the analyte level may be one or more of the rate of change of the potassium level, the rate of change of the glucose level, the rate of change of the lactate level, the rate of change of the phosphate level, the rate of change of the bicarbonate level, the rate of change of the calcium level, the rate of change of the magnesium level, the rate of change of the sodium level, and / or the rate of change of the blood urea nitrogen level. For example, the rate of change of the potassium level refers to the rate at which one or more timestamped potassium levels change relative to one or more other timestamped potassium levels. The rate of change of the analyte level may be determined over a period of one second or more, one minute or more, one hour or more, one day or more, etc.

[0108] Additionally or alternatively, a rate of change of a determined analyte level may be marked as "increasing rapidly" or "decreasing rapidly". As used herein, "rapidly" may describe a rate of change of an analyte level that is clinically significant and indicative of a tendency for a patient's analyte level to likely breach a threshold level within the next defined period.

[0109] A prediction tendency (e.g., generated by the decision support engine 114 using one or more trained models) may, in some cases, indicate, based on a rate of change of a determined analyte level, that a patient is likely to reach an absolute maximum analyte level, for example, within a particular imminent period. Thus, such a rate of change of an analyte level may be marked as "increasing rapidly". Similarly, a prediction tendency (e.g., generated by the decision support engine 114 using one or more trained models) may, in some cases, indicate, based on a rate of change of a determined analyte level, that a patient is likely to reach a lower threshold within a particular imminent period. Thus, such a rate of change of an analyte level may be marked as "decreasing rapidly".

[0110] Some medical treatments may affect the accurate measurement of the rate at which analyte levels change. Thus, during such treatments, it may be desirable to adjust which analyte trends are clinically significant and to indicate that a patient may breach a threshold level within a specified period. One such treatment is dialysis. Since dialysis treatment acts as a secondary analyte filter, a rate of change classified as "slowly decreasing" may actually be a "rapidly decreasing" rate of change. During the effective period of dialysis treatment, the predicted analyte trend may be adjusted to offset the effect of dialysis on the measured analyte rate of change. For example, a rate of change classified as "slowly increasing" may actually be a "rapidly increasing" rate of change. Additionally or alternatively, the adjustment may be based on the type of medical treatment. Additionally or alternatively, the adjustment may be based on different treatment parameters. Additionally or alternatively, the adjustment may be based on the measured analyte. Additionally or alternatively, the adjustment may be based on whether the analyte is increasing or decreasing. For example, during dialysis treatment, an adjustment may not be required for an increasing rate of change, but may be required (e.g., 50% faster) for a decreasing rate of change.

[0111] Additionally or alternatively, the baseline analyte rate of change can be determined from a baseline determined over time for a user. For example, the potassium dialysis baseline rate of change refers to the rate at which one or more timestamped potassium dialysis baselines for a user change relative to one or more other timestamped potassium dialysis baselines for the same user. The rate of change of an analyte can be determined over a period of 1 second or more, 1 minute or more, 1 hour or more, 1 day or more, etc. Additionally or alternatively, baseline values of an analyte at different times may be determined for a user. For example, the pre-dialysis baseline can be used to inform which dialysate composition is optimal. The between-dialysis baseline, e.g., the fasting morning baseline, can be useful in providing exercise and dietary recommendations, and as its value changes, it can be determined when and if another dialysis session is needed. A baseline of another analyte can be collected prior to exercise, and the type of exercise (duration, intensity, etc.) can be recommended based on the baseline. Also, the post-exercise analyte (or non-analyte) baseline can show how the user's exercise behavior helps the user's health. For example, the post-exercise blood pressure can show that exercise has helped to lower the blood pressure.

[0112] Additionally or alternatively, the analyte clearance rate can be determined from sensor data (e.g., analyte levels obtained from the continuous analyte sensor 202 of the continuous analyte monitoring system 104) following consumption of a known or estimated amount of the analyte. Additionally or alternatively, the analyte clearance rate can be determined by calculating the slope between an initial high analyte level (e.g., the highest analyte level during a period in which the analyte level is increasing) and a subsequent low analyte level (e.g., the lowest analyte level during a period after the analyte level has increased). Additionally or alternatively, the clearance rate of an analyte can be determined for one or more analytes including potassium, glucose, lactate, insulin, phosphate, bicarbonate, calcium, and magnesium, sodium, albumin, creatinine, and / or blood urea nitrogen. The analyte clearance rate calculated over time can be timestamped and stored in the user's profile 118.

[0113] Additionally or alternatively, the analyte clearance rate analyzed over time can indicate a change in renal function and / or homeostasis. For example, the slope of the curve of potassium clearance during a first period (e.g., after consumption of a known amount of potassium) compared to the slope of the curve of potassium clearance during a second period (e.g., after consumption of the same amount of potassium) can indicate the ability of the kidneys to function, more specifically, the ability to maintain potassium homeostasis (e.g., the potassium clearance rate can be slower when the user's kidneys are impaired than when the user's kidneys are healthy). The tendency of the slope of the curve of analyte data indicating the user's renal function over time can be determined by comparing the slopes of the curves of analyte data over many different periods (e.g., over 5 minutes, 10 minutes, 35 minutes, 1 hour, 1 day, 1 week, or 1 month). In another example, the change between the slopes of the curves of glucose clearance during a first period and a second period can indicate the ability of the kidneys to function and maintain glucose homeostasis.

[0114] Additionally or alternatively, the analyte clearance rate may be determined during a period in which the user is involved in an external event, condition, or activity that can affect the analyte clearance rate. For example, the analyte clearance rate may be determined during a treatment period of a medical treatment (e.g., dialysis, diuretics, insulin, etc.). The analyte clearance rate may also be monitored before or after the treatment period. Additionally or alternatively, the analyte clearance rate associated with an external event, condition, or activity may be used to indicate the effect that the external condition had on the analyte level (e.g., the effect of a medical treatment, the effect of exercise, the effect of food intake).

[0115] Additionally or alternatively, the analyte trend may be determined based on analyte levels measured over a specific period (e.g., potassium level over time, glucose level over time, lactate level over time, insulin level over time, phosphate level over time, bicarbonate level over time, calcium level over time, magnesium level over time, sodium level over time, blood urea level over time, etc.). Additionally or alternatively, the analyte trend may be determined based on the baseline of the analyte over time. Additionally or alternatively, the analyte trend may be determined based on the analyte level over time. Additionally or alternatively, the analyte trend may be determined based on the rate of change of the analyte over time. Additionally or alternatively, the analyte trend may be determined based on the clearance rate of the analyte over time.

[0116] Additionally or alternatively, 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 the user's cells respond to insulin. Improving insulin sensitivity for a user may help reduce insulin resistance in the user.

[0117] Additionally or alternatively, residual insulin on board can be determined using a known or learned (e.g., from user data) insulin time-action profile that can take into account analyte sensor data (e.g., insulin measurements obtained from the insulin sensor of the continuous analyte monitoring system 104), non-analyte sensor data inputs (e.g., insulin delivery information), and / or both basal metabolic rate (e.g., insulin uptake to maintain body movement) and insulin use driven by activity or food consumption.

[0118] Additionally or alternatively, health and disease metrics can be determined from physiological sensors (e.g., body temperature), activity sensors, or a combination thereof, based on, for example, one or more of user inputs (e.g., pregnancy information or known illness or disease information). Additionally or alternatively, based on the values of the health and disease metrics, for example, the user's state can be defined as one or more of healthy, ill, at rest, or fatigued.

[0119] Additionally or alternatively, disease stage metrics such as for kidney disease can be determined based on, for example, one or more of user inputs or outputs provided by the decision support engine 114 illustrated in FIG. 1. Additionally or alternatively, exemplary disease stages for kidney disease can include AKI, stage 1 CKD with normal or high GFR (e.g., GFR > 90 mL / min), stage 2 mild CKD (e.g., GFR = 60 - 89 mL / min), stage 3A moderate CKD (e.g., GFR = 45 - 59 mL / min), stage 3B moderate CKD (e.g., GFR = 30 - 44 mL / min), stage 4 severe CKD (e.g., GFR = 15 - 29 mL / min), and stage 5 end-stage CKD (e.g., GFR < 15 mL / min). Additionally or alternatively, exemplary disease stages can be represented as GFR values / ranges, severity scores, etc.

[0120] Additionally or alternatively, the dietary state metric may indicate the user's state with respect to food consumption. For example, the dietary state may indicate whether the user is in one of a fasting state, a pre-meal state, a feeding state, a post-meal reaction state, or a stable state. Additionally or alternatively, the dietary state may also indicate residual nutrition, such as a consumed meal, snack, or beverage, and may be determined from food consumption information, meal time information, and / or digestibility information, which may be correlated, for example, with the type, amount, and / or order of food (e.g., which food / beverage was eaten first).

[0121] Additionally or alternatively, the dietary habit metric is based on the content and timing of the user's meals. For example, if the dietary habit metric is on a scale of 0 to 1, in an embodiment, the better / more healthy the user's meal, the higher the user's dietary habit metric is relative to 1. Also, in an embodiment, the more the user's food consumption adheres to a specific time schedule or a recommended meal, the closer the user's dietary habit metric is to 1.

[0122] Additionally or alternatively, medical treatment compliance is measured by one or more metrics that indicate how committed the user is to their medical treatment regimen. Additionally or alternatively, the medical treatment compliance metric is calculated based on one or more of the timing of the medical treatment (e.g., whether the administration is on time or on schedule), the type of medical treatment (e.g., whether the administration is the correct type of medical treatment), and the treatment parameters of the medical treatment (e.g., whether the administration is with the correct treatment parameters). Additionally or alternatively, the user's medical treatment compliance may be determined by monitoring the implementation and timing of the medical treatment and the parameters of such medical treatment implementation. Monitoring the implementation of the medical treatment may involve receiving information about the treatment implementation from the medical device 208 and / or from the analyte monitoring system 104 through user input.

[0123] Additionally or alternatively, the activity level metric may indicate the user's activity level. Additionally or alternatively, the activity level metric is determined based on input from an activity sensor or other physiological sensor, such as, for example, non-analyte sensor 206. Additionally or alternatively, the activity level metric may be calculated by DAM 116 based on one or more of inputs 128, such as one or more of exercise information, non-analyte sensor data (e.g., accelerometer data), time, user input, etc. Additionally or alternatively, the activity level may be represented as the user's step rate. The activity level metric may be timestamped so as to be correlated with the user's analyte level simultaneously.

[0124] Additionally or alternatively, the exercise regimen metric may indicate one or more of what type of activity the user is engaged in, the corresponding intensity of such activity, the frequency with which the user is engaged in such activity, etc. Additionally or alternatively, 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, respiratory rate sensor, etc.), calendar input, user input, etc.

[0125] Additionally or alternatively, the body temperature metric may be calculated by DAM 116 based on input 128, more specifically, non-analyte sensor data from a temperature sensor. Additionally or alternatively, the heart rate metric (e.g., including heart rate and heart rate variability) may be calculated by DAM 116 based on input 128, more specifically, non-analyte sensor data from a heart rate sensor. Additionally or alternatively, the respiration metric may be calculated by DAM 113 based on input 128, more specifically, non-analyte sensor data from a respiratory rate sensor.

[0126] Exemplary methods and systems for providing decision-making support regarding diabetes and kidney disease FIG. 4 is a flow diagram illustrating an exemplary method 400 for providing decision support using a continuous analyte monitoring system that includes at least a continuous analyte sensor capable of monitoring at least one of glucose or potassium, according to certain exemplary aspects of the present disclosure. For example, method 400 may be performed to provide decision support to a user using a continuous analyte monitoring system 104 that includes at least a continuous analyte sensor 202, as illustrated in FIGS. 1 and 2.

[0127] Method 400 collects and / or generates data, such as inputs 128 and metrics 130, including, for example, analyte data, patient information, and non-analyte sensor data, during various periods (e.g., a treatment period, a pre-treatment period, and / or a post-treatment period) to create various corresponding physiological profiles that can be used by a decision support system 100 to assist in addressing the identified risk of adverse events by: (1) identifying the risk of adverse events during various periods based on the corresponding physiological profiles, (2) providing recommended treatment parameters for the administration of a medical treatment, and / or automatically controlling the operation of one or more medical devices (e.g., a dialysis device, an insulin pump, etc.) based on such recommended treatment parameters, to make patient-specific treatment decisions or recommendations.

[0128] For example, the decision support system 100 can execute method 400 by monitoring one or more analytes of a patient during a period before, during, or after a medical treatment. Next, the decision support system 100 can determine one or more analyte metrics (e.g., analyte change rate, analyte clearance rate, etc.) related to the monitored analytes before, during, and after the medical treatment period, and generate physiological profiles before, during, and after the treatment, respectively. These physiological profiles can indicate the patient's analyte metrics, such as the analyte change rate and / or the analyte clearance rate, during the corresponding periods. Therefore, once these profiles are created, the decision support system 100 can subsequently determine the likelihood that the patient will experience a health harmful event during the periods before, during, and after the treatment.

[0129] For example, by using a treatment profile created for a user by monitoring the user's analyte metrics during one or more previous treatment periods, the decision support system 100 can predict the likelihood of a harmful event during the current treatment period and determine the likelihood of a harmful event occurring during the treatment period based on the user's current analyte and / or non-analyte information, current treatment parameters, and other information. Based on the determined likelihood, the decision support system 100 can subsequently generate optimized treatment parameters and / or recommendations. For example, if the determined likelihood exceeds a specific threshold, the decision support system 100 can generate treatment parameters and / or recommendations optimized to reduce the likelihood of a harmful event.

[0130] As another example, by monitoring the analyte metrics of a user over one or more previous post-treatment periods and using the post-treatment profile created for the user, the decision support system 100 can predict the likelihood of an adverse event during the current post-treatment period and determine the likelihood of an adverse event occurring during the post-treatment period based on the user's current analyte and / or non-analyte information, the treatment parameters used during the treatment session, and other information. Based on the determined likelihood, the decision support system 100 can subsequently generate an optimized post-treatment recommendation. For example, if the determined likelihood exceeds a certain threshold, the decision support system 100 can generate a post-treatment recommendation optimized to reduce the likelihood of an adverse event.

[0131] As another example, by monitoring the analyte metrics of a user over one or more previous pre-treatment periods and / or mid-treatment periods and using the pre-treatment profile and / or treatment profile created for the user, the decision support system 100 can predict the likelihood of an adverse event during the current post-treatment period and determine the likelihood of an adverse event occurring during the post-treatment period based on the user's current analyte and / or non-analyte information, the treatment parameters used during the treatment session, and other information. Based on the determined likelihood, the decision support system 100 can subsequently generate an optimized post-treatment recommendation. For example, if the determined likelihood exceeds a certain threshold, the decision support system 100 can generate a post-treatment recommendation optimized to reduce the likelihood of an adverse event.

[0132] As yet another example, by monitoring an analyte metric of a user over one or more previous pretreatment periods and using a pretreatment profile created for the user, decision support system 100 can predict the likelihood of an adverse event during the current pretreatment period and determine the likelihood of an adverse event occurring during the pretreatment period based on the user's current analyte and / or non-analyte information, as well as other information. Based on the determined likelihood, decision support system 100 can subsequently generate an optimized pretreatment recommendation. For example, if the determined likelihood exceeds a particular threshold, decision support system 100 can generate a pretreatment recommendation optimized to reduce the likelihood of an adverse event.

[0133] In certain embodiments, a pretreatment profile created for a user can enable decision support system 100 to predict the likelihood of an adverse event during or after treatment based on the user's pretreatment analyte data. For example, a dialysis session with a higher flow rate may cause a high rate of change in insulin and / or glucose levels, and although an adverse event may not occur during dialysis, it can increase the likelihood of an adverse event (e.g., hypoglycemia) after dialysis treatment.

[0134] Additionally or alternatively, the decision support system 100 presented herein may be configured to predict the effect of a medical treatment on renal function, provide decision support for the management of medical treatments that affect renal function, and thereby provide the user's analyte clearance rate. In particular, a patient may experience a period during which the patient's renal function is different from, or reacts differently than, the patient's typical renal function due to a medical treatment that affects renal function. Examples include a deterioration in blood glucose status or hypertension that causes a decrease in renal function, or acute kidney injury that results in a rapid decrease in renal function. Thus, by predicting the effect of a medical treatment on a patient's renal function based on sensor data (e.g., generated by the continuous analyte sensor 202), the decision support system 100 presented herein can generate optimal treatment parameters for reducing the likelihood of adverse health effects associated with medical treatments that affect renal function, which can be important for improving patient care and reducing the deterioration of renal function.

[0135] Additionally or alternatively, the decision support engine 114 of the decision support system 100 can use various algorithms or artificial intelligence (AI) models, such as machine learning models trained based on patient-specific data and / or population data, to predict analyte metrics (e.g., rate of change and / or clearance rate) associated with pre-treatment, during-treatment, and / or post-treatment periods, and to provide decision support for the management of medical treatments. The algorithms and / or machine learning models may take into account one or more inputs 128 and / or metrics 130 described with respect to FIG. 3 for the patient when predicting the analyte metric (e.g., rate of change and / or clearance rate) and generating decision support for at least one of the pre-treatment, during-treatment, and / or post-treatment periods.

[0136] Additionally or alternatively, one or more machine learning models may include input single-output (MISO) models trained to predict metrics associated with different analytes during pre-treatment, treatment, and / or post-treatment periods, respectively. For example, one model may be trained to predict glucose clearance rate, and another model may be trained to predict potassium clearance rate.

[0137] Additionally or alternatively, one or more machine learning models may include input multi-output (MIMO) models trained to predict metrics associated with different analytes for different periods. For example, one model may be trained to predict both glucose clearance rate and potassium clearance rate. Additionally or alternatively, the model may be trained to output a vector having multiple values, where each value corresponds to a different predicted analyte clearance rate that the model is trained to generate.

[0138] Additionally or alternatively, one or more machine learning models may include MISO models trained to generate optimized medical treatment parameters (e.g., type, dosage, timing, frequency, composition, concentration, flow rate, volume, etc.) for a medical treatment. For example, one model may be trained to generate the type of optimized medical treatment, and another model may be trained to generate the frequency of the optimized medical treatment.

[0139] Additionally or alternatively, one or more machine learning models may include MIMO models trained to generate optimized medical treatment parameters (e.g., type, dosage, timing, frequency, composition, concentration, flow rate, volume, etc.) for a medical treatment. For example, a single model may be trained to generate the type and frequency of the optimized medical treatment. Additionally or alternatively, the model may be trained to output a vector having multiple values, where each value corresponds to a different treatment parameter that the model is trained to generate optimized treatment parameters for.

[0140] One or more machine learning models described herein for making such predictions can first be trained using population data. A method for training one or more machine learning models can be described in more detail below with respect to FIG. 5.

[0141] Additionally or alternatively, as an alternative to using a machine learning model, the decision support engine 114 may use a rule-based model to determine the effect of a medical treatment on a patient's physiological function (e.g., an analyte metric such as clearance rate), and provide decision support for the management of the medical treatment. A rule-based model involves using a set of rules for analyzing data. These rules often follow the line of "If X happens then do or conclude Y" and are thus often referred to as "If Statements". In particular, the decision support engine 114 may apply rule statements (e.g., if, then statements) to determine how a patient's physiological function may be affected by a medical treatment, and provide decision support for performing the medical treatment based on the determination.

[0142] Such rules can be maintained in a reference library by the decision support engine 114. For example, the reference library can maintain ranges of analyte clearance rates that can be mapped to different probabilities of adverse events. In another example, the reference library can maintain ranges of treatment parameters that can be mapped to different effects on analyte clearance rates. Additionally or alternatively, such rules can be determined based on empirical studies or analyses of patient medical records, such as records stored in the medical record database 112. In some cases, the reference library can be very granular. For example, other factors can be used in the reference library to create such "rules". Other factors can include gender, age, diet, medical history, family medical history, body mass index (BMI), etc. The increased granularity can provide a more accurate output.

[0143] In block 402, method 400 begins by continuously monitoring one or more analytes of a patient, such as patient 102 illustrated in FIG. 1. The one or more analytes to be monitored may include at least one of glucose and potassium. Block 402 may additionally or alternatively be performed by the continuous analyte monitoring system 104 illustrated in FIGS. 1 and 2, and more specifically, by the continuous analyte sensor 202 illustrated in FIG. 2. For example, the continuous analyte monitoring system 104 may include a continuous analyte sensor 202 configured to measure a patient's analyte levels before, during, and / or after a treatment period of a medical treatment. Examples of medical treatments include exercise, diet, drug intake, or dialysis. The effective period of a medical treatment may be one or more periods during which the medical treatment induces a biological response in the user. The biological response may include the activity, absorption, pharmacodynamics, affinity, and / or effectiveness of the medical treatment for the patient.

[0144] The primary analytes for measurement described herein are glucose and / or potassium, but additionally or alternatively, other analytes may be considered. In particular, combining analyte data from two or more analytes can further inform the effect of a medical treatment on a patient's physiology and can help provide decision-making support for the management of the treatment of patients with kidney disease. For example, monitoring additional types of analytes such as glucose, potassium, lactate, insulin, phosphate, bicarbonate, calcium, magnesium, sodium, albumin, creatinine, and / or BUN measured by the continuous analyte monitoring system 104 can provide further insight into the effect of a medical treatment on a patient's physiology and can provide decision-making support for such medical treatments.

[0145] Additionally or alternatively, additional insights obtained from using combinations of analytes can improve the accuracy of predicting the expected effects of medical treatments on a patient's physiological functions, such as adverse events. For example, the probability of accurately predicting the effect of a medical treatment on a patient's physiological functions can be a function of the number of analytes measured for the patient. In some examples, the probability of accurately predicting the effect of a medical treatment on a patient's physiological functions using only potassium data (in addition to other non-analyte data) can be less than the probability of accurately predicting the effect of a medical treatment on a patient's physiological functions using both potassium and glucose data (in addition to other non-analyte data), which can also be less than the probability of accurately predicting the effect of a medical treatment on a patient's physiological functions using potassium, glucose, and sodium data (in addition to other non-analyte data) for the analysis.

[0146] Additionally or alternatively, as described herein, combinations of analytes measured and collected by, for example, one (e.g., multi-analyte) or more sensors for predicting the effect of a medical treatment on a patient's physiological functions and providing decision support for treatment management and adjustment include at least two of glucose, potassium, lactate, insulin, phosphate, bicarbonate, calcium, magnesium, sodium, and BUN. However, other combinations of analytes can be considered. Since the kidneys process and metabolize many different analytes, additional analyte data can improve the accuracy of predictions regarding the effect of a medical treatment on a patient's physiological functions. In one example, the prediction can include predicting the likelihood of adverse events occurring during or after treatment.

[0147] For example, lactate levels may be related to glucose, insulin, and potassium metabolism. Lactate levels may also be used to detect food consumption, exercise, rest, infection, and / or stress. Thus, additional insights obtained from lactate levels, which can indicate metabolism and renal function, as well as different body states (e.g., ingested food, exercise, rest, infection, stress, etc.), can lead to the generation of a more complete physiological profile, which can then be used to determine the likelihood of adverse events during pre-treatment, treatment, and post-treatment periods. For example, measurements of glucose and lactate levels can be used to determine whether a patient is at risk of developing lactic acidosis, particularly when monitored in combination with dosing information.

[0148] In another example, calcium levels may be related to calcium metabolism. Calcium homeostasis is maintained by the kidneys, and calcium levels can be used to indicate renal function. Thus, additional insights obtained from calcium levels can lead to the generation of a more complete physiological profile, which can then be used to determine the likelihood of adverse events during pre-treatment, treatment, and post-treatment periods. In addition, calcium homeostasis may be affected by certain medical procedures (e.g., dialysis), and calcium levels can be used to generate optimal treatment parameters for such medical procedures.

[0149] In another example, an increase in phosphate levels in the blood (e.g., hyperphosphatemia) can be associated with CKD. Hyperphosphatemia (e.g., abnormally high serum phosphate levels) can result from increased phosphate intake, decreased phosphate excretion, or disorders that shift intracellular phosphate into the extracellular space. This increase in serum phosphate levels is associated with reduced renal ion excretion and the use of medications to reduce the progression of CKD or control related diseases such as type 2 diabetes and heart failure. Furthermore, phosphate homeostasis is maintained by the kidneys, and phosphate levels can be used to indicate renal function. Thus, additional insights obtained from phosphate levels can improve the generation of a more complete physiological profile, which can then be used to determine the likelihood of adverse events during the pre-treatment, treatment, and post-treatment periods. In addition, phosphate homeostasis can be affected by certain medical procedures (e.g., dialysis), and phosphate levels can be used to generate optimal treatment parameters for such medical procedures.

[0150] In yet another example, bicarbonate homeostasis is maintained by the kidneys, and bicarbonate levels can be used to indicate renal function. Thus, additional insights obtained from bicarbonate levels can result in the generation of a more complete physiological profile, which can then be used to determine the likelihood of adverse events during the pre-treatment, treatment, and post-treatment periods. In addition, bicarbonate homeostasis can be affected by certain medical procedures (e.g., dialysis), and bicarbonate levels can be used to generate optimal treatment parameters for such medical procedures.

[0151] In yet another example, magnesium homeostasis is maintained by the kidneys, and magnesium levels can be used to indicate kidney function. Thus, additional insights obtained from magnesium levels can lead to the generation of a more complete physiological profile, which can then be used to determine the likelihood of adverse events during the pre-treatment, treatment, and post-treatment periods. In addition, magnesium homeostasis may be affected by certain medical procedures (e.g., dialysis), and magnesium levels can be used to generate optimal treatment parameters for such medical procedures.

[0152] In yet another example, sodium homeostasis is maintained by the kidneys, and sodium levels can be used to indicate kidney function. Thus, additional insights obtained from sodium levels can lead to the generation of a more complete physiological profile, which can then be used to determine the likelihood of adverse events during the pre-treatment, treatment, and post-treatment periods. In addition, sodium homeostasis may be affected by certain medical procedures (e.g., dialysis), and sodium levels can be used to generate optimal treatment parameters for such medical procedures.

[0153] In another example, the liver breaks down proteins used by cells in the body and then produces ammonia, which contains nitrogen. Nitrogen combines with other elements, such as carbon, hydrogen, and oxygen, to form urea, which is a chemical waste product. Urea moves from the liver to the kidneys through the bloodstream. Healthy kidneys filter urea and remove other waste products from the blood, and the filtered waste products exit the body through urine. Thus, BUN levels (e.g., the level of nitrogen content in urea) can provide insights into kidney health and function. Thus, a patient experiencing high measured extracellular potassium is assumed to have impaired kidney function and may be expected to be experiencing high measured BUN (e.g., considering that a damaged kidney is likely unable to filter urea and remove other waste products from the blood). Thus, BUN levels can be used to indicate kidney function. Thus, additional insights obtained from BUN levels can lead to the generation of a more complete physiological profile, which can then be used to determine the likelihood of adverse events during the pre-treatment, treatment, and post-treatment periods. In addition, BUN metabolism may be affected by certain medical procedures (e.g., dialysis), and BUN levels can be used to generate optimal treatment parameters for such medical procedures.

[0154] In another example, creatinine is produced mainly as a byproduct of muscle and protein metabolism. It is removed by the kidneys and is thus a useful metric of kidney health. Thus, additional insights obtained from creatinine levels can lead to the generation of a more complete physiological profile, which can then be used to determine the likelihood of adverse events during the pre-treatment, treatment, and post-treatment periods.

[0155] In another example, cystatin C is a protein often used as a marker of kidney health even in the early stages of kidney disease. When kidney health begins to decline, even in the early stages of kidney disease, the amount of cystatin C in the body begins to increase. Thus, additional insights can be gained from cystatin C levels, leading to the generation of a more complete physiological profile, which can then be used to determine the likelihood of adverse events during the pre-treatment, treatment, and post-treatment periods.

[0156] In another example, serum albumin is a protein produced by the liver and maintains homeostasis, particularly extracellular fluid volume, or colloid osmotic pressure. Albumin levels can change significantly during dialysis and thus can affect blood pressure and fluid volume. Therefore, it is important to measure albumin during dialysis.

[0157] In addition, other analytes may be used to indicate other effects of medical treatments that affect a patient's kidney function (e.g., not kidney function effects). For example, lactate levels measured in proximity to a peritoneal dialysis port may indicate sepsis of the peritoneal dialysis port.

[0158] In addition to continuously monitoring one or more analytes of a patient over multiple periods to obtain analyte data in block 402, optionally, additionally or alternatively, method 400 may also include monitoring other sensor data (e.g., non-analyte data) over multiple periods using one or more other non-analyte sensors or devices (e.g., non-analyte sensor 206 and / or medical device 208 of FIG. 2, etc.).

[0159] As previously mentioned, the non-analyte sensors and devices can include, but are not limited to, one or more of an insulin pump, an acoustic sensor, a tactile sensor, an ECG sensor or heart rate monitor, a blood pressure sensor, a respiratory sensor, a peritoneal dialysis device, a hemodialysis device, 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 smartwatch), or any other sensor or device that provides related information about the user. Metrics such as the metric 130 illustrated in FIG. 3 can be calculated using measurement data from one or more of these additional sensors. As illustrated in FIG. 3, the metric 130 calculated from non-analyte sensor or device data can include heart rate (including heart rate variability), respiratory rate, and the like. Additionally or alternatively, as described in more detail below, the metric 130 calculated from non-analyte sensor or device data can be used to generate a physiological profile and thus further inform an analysis of medical treatments that affect a patient's physiological function.

[0160] Additionally or alternatively, one or more of these non-analyte sensors and / or devices may be worn by the user to assist in detecting periods of increased physical activity by the user. Such non-analyte sensors and / or devices may include accelerometers, ECG sensors, blood pressure sensors, blood oxygen / oximetry sensors, barometric pressure sensors, ambient oxygen sensors, heart rate monitors, impedance sensors, insulin pumps, dialysis devices (e.g., peritoneal dialysis devices, hemodialysis devices, etc.), sensors or devices provided by display device 107 (e.g., accelerometers, cameras, global positioning systems (GPS), heart rate monitors, etc.), or other user accessories (e.g., smartwatches), or any other sensors or devices that provide relevant information about the user. Analyte metrics such as analyte clearance rate and analyte level may be affected by exercise. Due to these effects, additionally or alternatively, analyte data collected during exercise may be excluded from the information used to determine the likelihood of a health-harmful event. However, additionally or alternatively, analyte data may be correlated with exercise, and the determination of the likelihood of a health-harmful event may be based on analyte data collected during exercise. For example, the determined likelihood of a health-harmful event during exercise may be based on analyte data collected during exercise. In some embodiments, collecting ambient sensor readings enables a decision support algorithm to take into account location-related features associated with the user's location when determining treatment recommendations and / or treatment parameters for the user.

[0161] Additionally or alternatively, one or more of these non-analyte sensors and / or devices may be worn by the user to assist in predicting adverse events over various periods. Additionally or alternatively, one or more non-analyte sensors and / or devices that may be worn by a patient may include a blood pressure sensor. Blood pressure measurements collected from the blood pressure sensor may be used to provide further insight into the likelihood of adverse events. In particular, CKD and hypertension are closely related. Typically, as blood pressure rises, kidney function declines. Thus, evaluation of a patient's blood pressure levels over various periods can provide further insight into the health of the patient's kidneys and, for example, can lead to a lower potassium clearance rate. Thus, a patient who experiences high measured extracellular potassium and is assumed to have impaired kidney function (e.g., considering that excessive potassium is not being filtered from the body) may also be expected to be experiencing high blood pressure levels.

[0162] Additionally or alternatively, one or more non-analyte sensors and / or devices that may be worn or used by a patient may include an ECG sensor and / or a heart rate monitor. As is known in the art, an ECG device is a device that measures the electrical activity of the heart. Any morphological or interval changes in the ECG signal can be used in combination with analyte data to provide a more accurate determination of the risk of health adverse events. Additionally or alternatively, heart rate measurements and heart rate variability information collected from the ECG sensor and / or heart rate monitor can be used in combination with analyte data to more accurately predict the risk of experiencing health adverse events such as hyperkalemia and / or one or more cardiac events (e.g., arrhythmia and / or sudden cardiac death).

[0163] Additionally or alternatively, the user may use or wear one or more analyte and / or non-analyte sensors and / or devices to help determine the start and end of each of the pre-treatment, treatment, and / or post-treatment periods. For example, non-analyte sensors and / or devices may be used to determine whether a medical procedure such as dialysis is in progress and to determine the start and / or end of the medical procedure. Such non-analyte sensors and / or devices may include accelerometers, ECG sensors, blood pressure sensors, heart rate monitors, impedance sensors, insulin pumps, dialysis devices, and the like. In some embodiments, the non-analyte sensors and / or devices may be worn or used only when a medical procedure is being performed or to perform a medical procedure. For example, a dialysis device may be used by a user only to perform a dialysis treatment. Thus, additionally or alternatively, a user wearing or using a non-analyte sensor and / or device may indicate that a medical procedure is currently being performed and that the user is currently in a treatment period. Similarly, additionally or alternatively, a user not wearing or using a non-analyte sensor and / or device may indicate that a medical procedure is not currently being performed and that the user is not currently within the effective period of a medical procedure. Additionally or alternatively, the non-analyte sensors and / or devices may be worn or used when no medical procedure is being performed. For example, an insulin pump may be continuously worn by a user even when insulin is not always being administered. Thus, in such embodiments, a user wearing or using a non-analyte sensor and / or device may not necessarily indicate that a medical procedure is being performed and / or whether the user is currently within the effective period of a medical procedure.

[0164] Additionally or alternatively, one or more non-analyte sensors and / or devices worn or used by a user may indicate treatment parameters and other medical treatment information related to a medical treatment. Such non-analyte sensors and / or devices may include an accelerometer, an ECG sensor, a blood pressure sensor, a heart rate monitor, an impedance sensor, an insulin pump, a dialysis device, and the like. Additionally or alternatively, the detected treatment parameters may form part of the medical treatment information provided as part of the input 128. As referred to herein, the medical treatment information may include the type, dosage, timing, frequency, and / or other similar treatment parameters (e.g., composition, concentration, flow rate, volume, etc.) of one or more drugs and / or treatments. Additionally or alternatively, the medical treatment may be administered to the user via the non-analyte sensor and / or device and may provide non-analyte data. For example, a dialysis device may be used to administer a dialysis treatment to the user and may also be used to provide non-analyte data. Additionally or alternatively, the treatment parameters are provided by the non-analyte sensor and / or device. Additionally or alternatively, the treatment parameters provided by the non-analyte sensor and / or device may be confirmed by the user.

[0165] In block 404, method 400 continues by processing analyte data to determine at least one analyte rate of change related to changes in one or more analytes. Additionally or alternatively, block 404 may be performed before, during, and / or after the treatment period of a medical treatment. For example, in some embodiments, one or more analyte metrics associated with the pre-treatment period of a medical treatment are determined and used to determine a pre-treatment profile for a patient. As another example, in some embodiments, one or more analyte metrics associated with the active period of a medical treatment are determined and used to determine a treatment profile for a patient. As a further example, in some embodiments, one or more analyte metrics associated with the post-treatment period of a medical treatment are determined and used to determine a post-treatment profile for a patient. Block 404 may additionally or alternatively be performed by the decision support engine 114.

[0166] As mentioned, the rate of change of an analyte refers to the rate that indicates the change of one or more time-stamped analyte levels relative to one or more other time-stamped analyte levels. Additionally or alternatively, the rate of change of an analyte is used to determine a physiological profile (e.g., a pre-treatment profile, a treatment profile, and / or a post-treatment profile of a patient). In some embodiments, at least one analyte rate of change for a patient may be calculated and used to create a physiological profile. For example, potassium levels and / or rates of change before, during, and after a medical treatment can be used as inputs to create pre-treatment, during-treatment, and post-treatment physiological profiles. In another example, glucose levels and / or rates of change before, during, and after a medical treatment can be used as inputs to create pre-treatment, during-treatment, and post-treatment physiological profiles.

[0167] In some embodiments, the analyte clearance rates before, during, and after a medical treatment can be used as inputs to create physiological profiles before, during, and after treatment. For example, the clearance rates of potassium, glucose, lactate, and / or other analytes described herein can be used as inputs for creating a physiological profile.

[0168] In block 406, method 400 continues by determining a set of physiological profiles based on at least one analyte metric determined in block 404 over various corresponding periods. Block 406 can be performed by the decision support engine 114 illustrated in FIG. 1. Additionally or alternatively, block 406 may be performed to create and update pre-treatment, treatment, and post-treatment profiles for a patient based on various triggers. For example, as described above, the decision support engine 114 can use analyte data, non-analyte data, and / or other types of data to determine the timing of the start and end of the treatment period, the timing of the start and end of the post-treatment period, and the timing of the start and end of the pre-treatment period. By way of example, the decision support engine 114 can continuously perform blocks 402 and 404 for a new patient over the first few weeks of a patient using the decision support system 100. During these first few weeks, the decision support engine 114 creates and updates the patient's pre-treatment, treatment, and post-treatment profiles using the data corresponding to each of these periods.

[0169] A physiological profile can describe one or more analyte metric patterns (e.g., analyte change rate pattern, clearance rate pattern, etc.) of a patient during a corresponding period. For example, a patient's treatment profile can show a historical pattern of the patient's analyte metrics during a treatment period. A patient's post-treatment profile can show a historical pattern of the patient's analyte metrics during a post-treatment period. The post-treatment period begins after the treatment period and is associated with a higher likelihood of adverse events resulting from the treatment. A patient's pre-treatment profile can show a historical pattern of the patient's analyte metrics during a period other than the treatment period and the post-treatment period.

[0170] Examples of analyte metrics include analyte clearance rates such as glucose clearance rate and potassium clearance rate. As discussed above, a user's analyte clearance rate may be different from the user's baseline analyte clearance rate during various periods. In some cases, a medical treatment (e.g., dialysis) can affect a patient's analyte clearance rate. For example, in some embodiments, a patient may have different glucose clearance rates before, during, and after a dialysis treatment. As another example, in some embodiments, a patient's potassium clearance rate may have different glucose clearance rates before, during, and after a dialysis treatment.

[0171] In some embodiments, for each patient, the decision support engine 114 creates and updates three physiological profiles, namely, a pre-treatment profile, a treatment profile, and a post-treatment profile. The treatment profile describes one or more patterns of the patient's analyte metrics during the treatment period of a medical treatment. The post-treatment profile describes one or more patterns of the patient's analyte metrics during a post-treatment period following the treatment period. The post-treatment period may be associated with an increased likelihood of adverse events resulting from the treatment. In some embodiments, the post-treatment period is a period having a predetermined length after the treatment period, e.g., a period of one minute or more, one hour or more, one day or more, or one week or more after the effective period.

[0172] In some embodiments, one or more physiological measurements of a patient (e.g., the patient's heart rate, the patient's body temperature, etc.) after the treatment period of the treatment are measured, and it is determined whether the physiological measurements meet the requirements of the post-treatment period. If the physiological measurements of the patient after the treatment period meet the requirements of the post-treatment period, the patient is determined to be in the post-treatment period. However, as soon as the patient's physiological measurements change and those measurements no longer meet the requirements of the post-treatment period, the patient is determined to be outside the post-treatment period and in the pre-treatment period. The pre-treatment profile describes one or more patterns of analyte metrics of the patient during the pre-treatment period, which is the period other than the treatment period and the post-treatment period.

[0173] The profile created for the patient when the patient first begins using the decision support system 100 is then continuously updated by the decision support engine 114. For example, consider a patient who starts using the decision support system 100 on day 1, does not receive dialysis treatment on day 1, receives dialysis treatment on day 2, does not receive dialysis treatment on day 3, receives dialysis treatment on day 4, and does not receive dialysis treatment on days 5 - 6. In this example, if the post-treatment period has a predetermined length of one day, the decision support engine 114 can first generate a pre-treatment profile of the patient based on the analyte metrics associated with day 1. On day 2, the pre-treatment profile is updated using data related to the period before the treatment period. When the treatment starts on day 2, the decision support engine 114 can generate a treatment profile of the patient based on the analyte metrics associated with the treatment period on day 2. Next, the decision support engine 114 can generate a post-treatment profile of the patient based on the rate of change of the analyte associated with the period after a portion of the treatment on days 2 and 3 has ended.

[0174] As further described below, using the profiles created and updated over the first, second, and third days, the decision support engine 114 can predict the likelihood of adverse events and / or provide decision support outputs (e.g., optimal treatment parameters, recommendations, etc.) during the corresponding periods of the fourth, fifth, and sixth days. For example, using the patient's treatment profile, the decision support engine 114 can predict the likelihood of adverse events during the treatment period on the fourth day and / or provide a decision support output. Thereafter, the decision support engine 114 can use the post-treatment profile to predict the likelihood of adverse events during the post-treatment periods associated with the fourth and fifth days and / or provide a decision support output. Thereafter, the decision support engine 114 can use the pre-treatment profile to predict the likelihood of adverse events on the sixth day and / or provide a decision support output. Between each of these days, the decision support engine 114 also continues to update the patient's profile using data collected over the corresponding periods.

[0175] Additionally or alternatively, in block 402, the continuous analyte monitoring system 104 can continuously monitor the patient's glucose and potassium levels during the pre-treatment, treatment, and post-treatment periods. Additionally or alternatively, the potassium data and glucose data for each period can then be used to determine glucose and potassium metrics, such as glucose and potassium clearance rates, during future similar periods and provide decision support for the management of the medical treatment. For example, the potassium data and glucose data can indicate (1) the patient's analyte clearance rates before, during, and / or after the effective period of the medical treatment, and (2) the likelihood of health adverse events occurring before, during, and / or after the effective period of the medical treatment.

[0176] In one particular example, glucose and / or potassium measurements generated during a treatment period of a medical treatment can be used to determine the glucose and / or potassium clearance rate during the treatment period. The glucose and / or potassium clearance rate determined for the treatment period is then used to generate or update a treatment profile that reflects the pattern of the patient's glucose and / or potassium clearance rate over one or more treatment periods associated with one or more treatment sessions. The generated or updated treatment profile can then be used to determine the likelihood that a patient will experience an adverse event during the duration of a future medical treatment (e.g., a future medical treatment of the same treatment type).

[0177] As another example, glucose and / or potassium measurements generated during a post-treatment period following a treatment period of a medical treatment can be used to determine the analyte glucose and / or potassium rate for the post-treatment period. The glucose and / or potassium rate determined for the post-treatment period is then used to generate or update a post-treatment profile that reflects the pattern of the patient's glucose and / or potassium clearance rate over one or more post-treatment periods. The generated or updated post-treatment profile can then be used to determine the likelihood that a patient will experience an adverse event during the post-treatment period of a future medical treatment (e.g., a future medical treatment of the same treatment type).

[0178] As yet another example, if glucose and / or potassium measurements are within a pre-treatment period, the glucose and / or potassium measurements can be used to determine an analyte clearance rate for the pre-treatment period. The glucose and / or potassium clearance rate determined for the pre-treatment period is then used to generate or update a pre-treatment profile that reflects the pattern of the patient's glucose and / or potassium clearance rate over one or more pre-treatment periods. The generated or updated pre-treatment profile can then be used to determine the likelihood that a patient will experience an adverse event during the pre-treatment period of a future medical treatment (e.g., a future medical treatment having the same treatment type). Additionally, the pre-treatment glucose and / or potassium measurements can be used to customize the user's treatment session in order to prevent an adverse event.

[0179] Method 400 continues, at block 408, by the decision support engine 114 determining the likelihood that the patient will experience a health adverse event during the current time (i.e., the time at which the decision support engine 114 is making the determination), based on the physiological profile generated at block 406. In particular, the decision support engine 114 can (i) classify the current time based on its relationship to the treatment period of the treatment (i.e., classify the current time as one of a treatment period, a pre-treatment period, or a post-treatment period), (ii) determine the physiological profile for the current time based on the classification of the current time, (iii) extract the pattern of analyte metrics described by the physiological profile for the current time, and (iv) determine the likelihood of a health adverse event based on the extracted pattern of analyte metrics.

[0180] The pattern of analyte metrics indicates metrics that may be expected to be experienced by the user during the corresponding period, and thus can be used to predict the likelihood that the user will experience an adverse event. In such an example, the pattern of analyte metrics may include the average analyte change rate, the average analyte clearance rate, the analyte change rate at different times during the corresponding period, the analyte clearance rate at different times during the corresponding period, the standard deviation of the analyte level during a specified period, the average standard deviation of the analyte level over subsequent periods, and the like. The health adverse event may additionally or alternatively be one or more of hypokalemia, hyperkalemia, hypoglycemia, hyperglycemia, cardiac events, death, and the like. In one example, the likelihood of a hyperkalemia event may increase if the predicted potassium clearance rate at the current time is below the threshold potassium clearance rate required to maintain the target level of the patient's health.

[0181] For example, in some embodiments, the decision support engine 114 determines whether the current time is in a pre-treatment period, a treatment period, or a post-treatment period. If the decision support engine 114 determines that the current time is during the treatment period, the decision support engine 114 determines the likelihood of an adverse event for the current time based on the pattern of analyte metrics described by the treatment profile generated and / or updated in block 406. Further, if the decision support engine 114 determines that the current time is in the post-treatment period, the decision support engine 114 determines the likelihood of an adverse event for the current time based on the pattern of analyte metrics described by the user's post-treatment profile. Further, if the decision support engine 114 determines that the current time is in the pre-treatment period, the decision support engine 114 determines the likelihood of an adverse event for the pattern of analyte metrics described by the pre-treatment profile.

[0182] As mentioned, different methods for determining the likelihood that a patient will experience a health - adverse event can be used by the decision - support engine 114. Additionally or alternatively, a rule - based model may be used. A rule - based model can include rules that take into account the current period in which the user is, the user's current analyte levels, treatment parameters, physiological profiles, and / or other factors. Treatment parameters can indicate the type of medical treatment, dosage, activity rate, activity duration, and / or timing. The user's current analyte levels can indicate the user's current potassium and / or glucose analyte levels, as well as other analytes including lactate, insulin, phosphate, bicarbonate, calcium, magnesium, sodium, and / or BUN. For example, using a rule - based model, the decision - support engine 114 can determine that when the user is in a treatment period, the user's current potassium and / or glucose levels are X and / or Y, the user's treatment profile indicates a Z clearance rate, and the user's treatment parameters for a treatment session are W, the likelihood that the user will experience an adverse event is Q. In another example, using a rule - based model, the decision - support engine 114 can determine that when the user is in a post - treatment period, the user's current potassium and / or glucose levels are X and / or Y, the user's post - treatment profile indicates a Z clearance rate, and the user's treatment parameters for a treatment session were W, the likelihood that the user will experience an adverse event is Q. In yet another example, using a rule - based model, the decision - support engine 114 can determine that when the user is in a pre - treatment period, the user's current potassium and / or glucose levels are X and / or Y, and the user's pre - treatment profile indicates a Z clearance rate, the likelihood that the user will experience an adverse event is Q. A rule - based model can be more granular and can include many other rules associated with the user's demographic information and other relevant parameters.

[0183] Additionally or alternatively, a machine learning model can be used to predict the likelihood that a user will experience an adverse event during a period of time. Input 128 and / or metric 130, as described with respect to FIG. 3, including the user's current analyte level, treatment parameters, predicted pattern of analyte metrics generated at block 406 (as indicated by the corresponding physiological profile), and / or other relevant data points (e.g., demographic information), may be used by the machine learning model to predict the likelihood that the user will experience an adverse event during a period of time. For example, the model may be trained using a training data set of past patient records that each (1) indicate the analyte level, timestamped analyte levels, treatment parameters, pattern of analyte metrics (timestamped analyte clearance rate), and / or other relevant data points (e.g., demographic information) of past patients, and (2) are labeled with the likelihood of an adverse event (100% indicates an adverse event that actually occurred).

[0184] Method 400 continues at block 410 by generating, by decision support engine 114, one or more recommendations and / or optimized treatment parameters based on the likelihood determined at block 408. Additionally or alternatively, decision support engine 114 can generate optimized treatment parameters for the management of medical treatment using one or more machine learning models trained based on patient-specific data and / or population data. The machine learning model can take into account one or more inputs 128 and / or metrics 130 described with respect to FIG. 3 for the patient to determine optimal recommendations for optimized treatment parameters and reduce the determined likelihood of a health adverse event during a particular period of time. Additionally or alternatively, as an alternative to using a machine learning model, decision support engine 114 may use one or more decision trees to provide optimized treatment parameters. The decision trees may be rule-based and may provide optimized treatment parameters to reduce the determined likelihood of a health adverse event during a period of time (e.g., treatment period, pre-treatment period, or post-treatment period).

[0185] The optimized treatment parameters may be generated by the decision support engine 114 and, in some embodiments, may be recommended to the user or automatically used in controlling the operation of a medical device such as a treatment delivery device (e.g., a dialysis device), as further described in connection with block 412. The treatment parameters optimized for managing a medical treatment may, in some cases, be based on the patient's physiological profile, current analyte levels, likelihood of determined health adverse events, current treatment parameters, and / or other relevant information. For example, the optimized treatment parameters may be recommended based on the likelihood of health adverse events determined at block 408. Additionally or alternatively, the recommended treatment parameters may include types, dosages, activity rates, activity durations, timings, concentrations, compositions, flow rates, volumes, and / or any other treatment parameters associated with a medical treatment.

[0186] Additionally or alternatively, the medical treatment may be dialysis, and the decision support engine 114 may generate optimized treatment parameters for dialysis. The optimized treatment parameters may include the type of dialysate, including composition and / or concentration, the type of dialysis membrane, flow rate, timing of treatment, frequency of treatment, length of treatment, and / or any other parameters of the dialysis treatment that can be adjusted to reduce the likelihood of health adverse events during the treatment session. Adjustment of the dialysis parameters during treatment may enable reduction of health adverse events.

[0187] Flow rate is the rate at which dialysate flows through a dialysis device and filters a patient's blood. Increasing the flow rate can increase the rate at which analytes are filtered from the patient's blood. The flow rate may be optimized to increase or decrease the filtration of analytes from the patient's blood to reduce the likelihood of a health - harmful event. For example, the flow rate can be optimized by increasing the flow rate to increase the filtration of an analyte (e.g., potassium) to reduce the risk of a health - harmful event (e.g., hyperkalemia). In another example, the flow rate can be optimized by increasing the flow rate (e.g., increasing from 200 mL / min to 300 mL / min or more) to reduce the risk of hypoglycemia during or after dialysis treatment for patients with a history of low glucose levels and / or hypoglycemia. Alternatively, the flow rate can be optimized by decreasing the flow rate (e.g., decreasing from 300 - 500 mL / min to 200 mL / min) to reduce the risk of hyperglycemia during or after dialysis treatment for patients with a history of high glucose levels and / or hyperglycemia.

[0188] Furthermore, hemodialysis can cause the release of potassium from red blood cells due to the shear stress that is disrupted by high flow rates. The optimized flow rate may be a reduced flow rate to reduce shear stress and potassium release during dialysis. For example, the optimized flow rate may be a reduced flow rate to prevent sheering of red blood cells. The flow rate may also be optimized to reduce the so - called "rebound" effect where certain electrolytes such as potassium can increase after a dialysis session.

[0189] Dialysate is a mixture that flows through a hemodialysis device or circulates through a catheter in a patient's abdominal cavity in peritoneal dialysis. Dialysis absorbs analytes from the patient's blood, thereby reducing serum analyte concentration. The composition and concentration of the dialysate can affect the amount and rate of analyte movement between the dialysate and the patient's blood. The composition and / or concentration of the dialysate can be optimized to reduce the risk that analyte movement is either too high or too low. For example, an optimized dialysate concentration may be a reduction in dextrose concentration to reduce the patient's glucose level and prevent hyperglycemia.

[0190] Hemodialysis treatments are often performed according to a weekly schedule in which sessions, which are typically performed multiple times a week (e.g., two to three times), continue for a predetermined length (e.g., one hour). The treatment schedule is often prescribed to the patient and not adjusted based on the patient's specific characteristics. Optimizing treatment parameters may include adjusting the dialysis treatment schedule, such as timing, length, and frequency. For example, the patient's convenience for dialysis treatment may change (e.g., due to work, family emergencies, financial situation, transportation, etc.), which may result in a change in the dialysis treatment schedule. In certain embodiments, the patient may be notified based on the patient's specific characteristics when there is a risk of potentially harmful events if the patient does not reschedule and / or complete dialysis treatment within a specific period (e.g., three days).

[0191] Additionally or alternatively, elevated analyte levels (current or predicted) and / or decreased analyte clearance rates (as indicated by the user's corresponding physiological profile) may indicate the need for a longer dialysis session, and thus the optimized treatment parameters may be to extend the dialysis treatment (e.g., by 50%). Additionally or alternatively, decreased analyte levels (current or predicted) and improved renal function (current or predicted) may indicate a desire for a shorter dialysis session, and thus the optimized treatment parameters may be to shorten the dialysis treatment (e.g., by 25%). Additionally or alternatively, a decrease in glucose level at the start of dialysis treatment may indicate that a higher flow rate and / or a particular type of dialysate are desirable, and thus the optimized treatment parameters may be to increase the flow rate for the dialysis treatment (e.g., to 300 - 500 mL / min) and / or to use a dialysate that increases the glucose level. If the patient is at high risk of hypoglycemia during or after dialysis treatment (e.g., based on current glucose level, history of hypoglycemia, or data from previous dialysis treatment sessions demonstrating a risk of hypoglycemia), the optimized dialysis treatment parameters may be to slightly increase the flow rate (e.g., from 200 mL / min to 300 mL / min or more). Additionally or alternatively, an increase in glucose level at the start of dialysis treatment may indicate that a slower flow rate is desirable, and thus the optimized treatment parameters may be to decrease the flow rate of the dialysis treatment (e.g., to 200 mL / min).

[0192] Additionally or alternatively, a patient's insulin sensitivity and / or residual insulin concentration can assist in determining optimized dialysis treatment parameters. For example, by monitoring a patient's insulin concentration before, during, and / or after treatment, particularly after a dialysis treatment session, insulin administration recommendations can be notified. Further, insulin sensitivity can be determined during dialysis by monitoring glucose and insulin concentrations during dialysis and comparing them to the pre-dialysis glucose and insulin concentrations. Based on the in-dialysis glucose and insulin concentrations, the decision support engine 114 can predict post-treatment insulin sensitivity and notify insulin administration recommendations.

[0193] Currently, peritoneal dialysis treatment is often performed at night while the patient is sleeping. Further, currently, as with hemodialysis, for peritoneal dialysis treatment, the treatment schedule is often not adjusted based on a patient's specific circumstances (e.g., renal function, risk of health adverse events, etc.). However, the embodiments described herein enable optimizing these treatment parameters based on the inputs described above. For example, additionally or alternatively, the user's current analyte levels, the user's corresponding physiological profile, determination of the likelihood of adverse events, etc. may indicate that a shorter or delayed peritoneal dialysis treatment should be considered. Additionally or alternatively, the length of the treatment may be based on the user's analyte levels reaching or exceeding a desired analyte level instead of a determined length of time. For example, the optimal treatment parameters may be to continue the dialysis treatment until the analyte level reaches a desired level (e.g., 3 moll / L of potassium) and then stop the dialysis treatment. Additionally or alternatively, the length of the treatment may be based on the rate of change or predicted trend (e.g., an adjusted predicted trend) of the analyte levels reaching or exceeding a desired rate of change of the analyte, and then the dialysis treatment may be stopped.

[0194] Additionally or alternatively, the medical treatment may involve the administration of a diuretic, and the decision support engine 114 may generate treatment parameters optimized for the administration of the diuretic. In such cases, the treatment parameters can include the type, dosage, frequency, timing, and / or similar treatment parameters, which can be optimized to reduce the potential for adverse health during the effective period of the diuretic treatment.

[0195] Additionally or alternatively, the recommended type of diuretic can be either potassium-sparing or non-potassium-sparing. Additionally or alternatively, the recommended type of diuretic may be based on the determined likelihood of adverse health events such as hypokalemia and / or hyperkalemia. Non-potassium-sparing diuretics lower potassium levels. For example, if a patient has a high risk of hyperkalemia, a non-potassium-sparing diuretic may be recommended to reduce the risk of hyperkalemia. However, for example, if the risk of hypokalemia is increasing, a potassium-sparing diuretic may be recommended to avoid the increased risk of hypokalemia. However, if a patient has developed end-stage renal disease and is undergoing dialysis treatment, diuretics may not be useful for the patient because they require a functioning kidney for effectiveness.

[0196] Additionally or alternatively, based on the determined likelihood of adverse health events, the frequency and / or timing of the diuretic may be recommended. Diuretics are often used to lower blood pressure. Additionally or alternatively, the treatment parameters may be optimized based on opposing risks of adverse health events. For example, non-analyte data may indicate the need to administer a diuretic to reduce the risk of hypertension, while analyte data may indicate the need to avoid administering a diuretic to reduce the likelihood of adverse health events based on the analyte data. In such cases, the recommended treatment parameters may additionally or alternatively be to delay the administration of the diuretic until the analyte level can be increased, and to recommend increasing the analyte level (e.g., through consumption).

[0197] Additionally or alternatively, the dosage of the diuretic can be optimized and recommended based on the determined likelihood of health adverse events. Optimal treatment parameters can be adjusting the dosage, such as reducing the dosage, increasing the dosage, and / or adjusting the dosage and other treatment parameters (e.g., frequency, type, timing, etc.) to reduce the likelihood of health adverse events. For example, the optimal treatment parameter can be reducing the dosage of the diuretic to reduce the likelihood of hypokalemia. In another example, the optimal treatment parameters can be reducing the dosage of the diuretic and potassium consumption to reduce the likelihood of hypokalemia. Additionally or alternatively, one or more optimized treatment parameters may be recommended together. Additionally or alternatively, the optimized treatment parameters may depend on available adjustments (e.g., only certain parameters may be changed), type of treatment (e.g., hemodialysis, peritoneal dialysis, diuretics, etc.), user input, and other optimized parameters. For example, the treatment parameters optimized for hemodialysis may be different from those optimized for peritoneal dialysis. In another example, only the frequency and timing of diuretic administration may be the treatment parameters optimized for the diuretic, but the dosage of the diuretic may be constant or may be set by user input (e.g., by the HCP).

[0198] Additionally or alternatively, the recommendations may also include decision support recommendations to assist the user in preventing and / or reducing the likelihood of health adverse events during the treatment and / or post-treatment period of the medical treatment, including food intake recommendations, exercise recommendations, and other medical treatment recommendations. For example, additionally or alternatively, the optimized treatment parameter may be an increase in potassium level, and the user may be recommended to consume potassium (e.g., potassium supplements) to increase the user's potassium level during the treatment and / or post-treatment period and reduce the likelihood of hypokalemia.

[0199] Additionally or alternatively, the optimized treatment parameters may be accompanied by a recommendation to consult with an HCP regarding the optimized treatment parameters. For example, the optimized treatment parameters may be to discontinue the use of a medical treatment. In some cases, the decision support engine 114 may also be able to recommend to the patient to consult with an HCP before discontinuing the use of a medical treatment. Additionally or alternatively, the optimized treatment parameters may be provided to an HCP.

[0200] Additionally or alternatively, the optimized treatment parameters may be generated for a subsequent treatment period. Additionally or alternatively, the subsequent treatment parameters may be based on the optimized treatment parameters of a prior treatment period. For example, treatment parameters optimized for an effective treatment period with reduced likelihood of a health adverse event may be recommended as initial treatment parameters for a subsequent effective treatment period.

[0201] Method 400 may continue by, in block 412, controlling the operation of the connected treatment device using one or more of the optimized treatment parameters, and / or, in block 414, providing one or more recommended and / or optimized treatment parameters to the user. As described above, medical device 208 may be part of continuous analyte monitoring system 104. Examples of medical device 208 may include a dialysis device, an insulin pump, or other treatment devices. As described above, in the case of a dialysis device, the optimized treatment parameters may include optimized type, dosage, activity rate, activity duration, timing, concentration, composition, flow rate, volume, and / or other treatment parameters that may be associated with the dialysis device. The operation of the dialysis device may be controlled by decision support engine 114 either directly or through the patient's display device (e.g., display device 107), transmitting the optimized treatment parameters to the dialysis device, and operating the dialysis device according to the treatment parameters. For example, the optimized treatment parameter may be the optimized flow rate determined in block 410. When the optimized treatment parameter is received by the dialysis device, the dialysis device adjusts its "current" flow rate (i.e., the flow rate at which treatment is being administered to the patient) to reach the optimized flow rate.

[0202] Additionally or alternatively, one or more of the recommended and / or optimized treatment parameters may be provided to the user via application 106. For example, any of the recommended and / or treatment parameters described in relation to block 410 may be provided to the user through the user interface of application 106, thereby enabling the user to manually change the operation of the medical device (e.g., dialysis device), manually adjust the administration of a treatment, and / or follow the recommendations (e.g., food intake recommendations, etc.).

[0203] Furthermore, method 400 may flow continuously from block 402 through block 410 during various periods to continuously control the operation of a connected therapy device using one or more of the optimized therapy parameters in block 410 and / or to provide recommendations including one or more of the generated optimized therapy parameters. The therapy parameters may be continuously optimized to reduce the likelihood of health adverse events during the effective period of a medical treatment and may continuously monitor analyte data.

[0204] As described herein, the machine learning models deployed by the decision support engine 114 include one or more models trained by the training system 140 as shown in FIG. 1 and provide various types of predictions described in connection with FIG. 4. FIG. 5 further details techniques for training one or more machine learning models to predict (1) the risk of adverse events during various periods based on corresponding physiological profiles and (2) patient-specific treatment decisions or recommendations to assist in addressing the identified risk of adverse events. Note that different models may be trained for each of the above predictions or outputs.

[0205] Method 500 begins at block 502 by retrieving data from a medical record database, such as medical record database 112 illustrated in FIG. 1, by a training system, such as training system 140 illustrated in FIG. 1. As referred to herein, medical record database 112 may provide a repository of up-to-date information and medical history information for (1) users of a continuous analyte monitoring system and connected mobile health applications, such as users of continuous analyte monitoring system 104 and application 106 illustrated in FIG. 1, and / or (2) one or more users who are not, or have not previously been, users of continuous analyte monitoring system 104 and / or application 106.

[0206] The retrieval of data from the medical record database 112 by the training system 140 may include, at block 502, the retrieval of all or any subset of the information maintained by the medical record database 112. For example, if the medical record database 112 stores information about 100,000 patients (e.g., non-users and users of the continuous analyte monitoring system 104 and the application 106), the data retrieved by the training 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.

[0207] As an example, integration with an in-house or cloud-based medical record database through, for example, Fast Healthcare Interoperability Resources (FHIR), a web application programming interface (API), Health Level 7 (HL7), and / or other computer interface languages may enable the aggregation of medical history records for baseline assessment, in addition to the aggregation of anonymized patient data from a cloud-based repository.

[0208] As an illustrative example, in block 502, the training system 140 retrieves information about 100,000 patients who have or have not had various medical treatments known to change or not change renal function and to change the analyte metrics (e.g., analyte clearance rate or rate of change) of the patients in the medical record database 112, predicts the effect of the medical treatment on the patients' analyte levels, predicts the likelihood of health-harmful events, and / or trains one or more models for generating recommendations and / or optimal treatment parameters for such treatments. Each of the 100,000 patients may have corresponding data records (e.g., based on their corresponding user profiles) stored in the medical record database 112. Each user profile 118 may include information such as the information discussed with respect to FIG. 3.

[0209] The training server system 140 then uses the information in each of the records to train an artificial intelligence or ML model (referred to herein as an "ML model" for simplicity). Examples of the types of information included in the patient's user profile were provided above. The information in each of these records can be characterized (e.g., manually or by the training server system 140) and result in features that can be used as input features for training the ML model. For example, the patient record may include or be used to generate features regarding the patient's age, the patient's gender, the patient's occupation, the patient's analyte levels over time, the rate of change and / or trend of the patient's analyte levels over time, physiological parameters associated with different health-harmful events of the patient over time, and / or any information provided by the input 128 and / or the metric 130. The features used to train the machine learning model may vary in different embodiments.

[0210] In certain embodiments, each medical history patient record retrieved from the medical history database 112 is further associated with a label indicating whether the patient was healthy or experienced some variation of the kidney disease, and whether the patient experienced an adverse event during a period, treatment, and / or similar metric. What records are labeled depends on what the model is trained to predict.

[0211] In block 504, method 500 continues by the training system 140 training one or more machine learning models based on the features and labels associated with the medical history patient records. In some embodiments, the training server does this by providing the features as inputs to the model. This model can be a new model initialized with random weights and parameters, or can be partially or fully pre-trained (e.g., based on previous training rounds). Based on the input features, the model-in-training generates some output. Additionally or alternatively, the output may indicate patient-specific treatment decisions or recommendations to assist in (1) the risk of adverse events during various periods, or (2) addressing the identified risk of adverse events, based on the corresponding physiological profile.

[0212] Additionally or alternatively, the training system 140 compares this generated output with the actual labels associated with the corresponding medical history patient records to calculate a loss based on the difference between the actual and the generated results. This loss is then used to refine one or more internal weights and parameters of the model (e.g., via backpropagation) so that the model learns to more accurately predict the risk of adverse events (or its recommended treatment) during various periods.

[0213] One of various machine learning algorithms can be used to train the model described above. For example, one of a supervised learning algorithm, a neural network algorithm, a deep neural network algorithm, a deep learning algorithm, etc. can be used.

[0214] In block 506, the training system 140 deploys a trained model to make predictions associated with kidney disease during runtime. In some embodiments, this includes transmitting some indication (e.g., a weight vector) of the trained model that can be used to instantiate the model on another device. For example, the training system 140 may transmit the weights of the trained model to the decision support engine 114. The model can then be used to predict (1) the risk of adverse events over various periods based on the corresponding physiological profile, and / or (2) patient-specific treatment decisions or recommendations to help address the identified risk of adverse events.

[0215] Furthermore, a similar method for training as shown in FIG. 5 using patient medical records may also be used to train a model using patient-specific records to create a more personalized model for making predictions related to (1) the risk of adverse events over various periods based on the corresponding physiological profile, and / or (2) patient-specific treatment decisions or recommendations to help address the identified risk of adverse events. For example, a model trained using patient medical records deployed for a particular user may be further retrained after deployment. For example, the model may be retrained after it has been deployed for a particular patient to create a more personalized model for that patient. The more personalized model can make more accurate predictions related to (1) the risk of adverse events over various periods based on the corresponding physiological profile, and / or (2) patient-specific treatment decisions or recommendations to help address the identified risk of adverse events.

[0216] FIG. 6 is a block diagram depicting a computing device 600 configured to (1) predict the effect of a medical treatment on renal function and (2) provide decision support for the management of treatments that affect renal function (e.g., predict optimal treatment, provide recommendations, etc.). Although depicted as a single physical device, in embodiments, computing device 600 may be implemented using virtual devices and / or across several devices such as a cloud environment. As illustrated, computing device 600 includes a processor 605, a memory 610, a storage 615, a network interface 625, and one or more I / O interfaces 620. In the illustrated embodiment, processor 605 fetches and executes programming instructions stored in memory 610, and stores and fetches application data present in storage 615. Processor 605 generally represents a single CPU and / or GPU, multiple CPUs and / or GPUs, a single CPU and / or GPU having multiple processing cores, etc. Memory 610 is generally included to represent random access memory (RAM). Storage 615 can be any combination of, for example, a disk drive, a flash-based storage device, etc., and can include fixed and / or removable storage devices such as a fixed disk drive, a removable memory card, a cache, optical storage, network attached storage (NAS), or a storage area network (SAN).

[0217] In some embodiments, input and output (I / O) devices 635 (such as keyboards, monitors, etc.) can be connected via the I / O interface 620. Further, via the network interface 625, the computing device 600 can be communicatively coupled to one or more other devices and components, such as the user database 110 and / or the medical record database 112. Additionally or alternatively, the computing device 600 is communicatively coupled to other devices via a network that may include the Internet, a local network, etc. The network can include a wired connection, a wireless connection, or a combination of wired and wireless connections. As illustrated, the processor 605, the memory 610, the storage 615, the network interface 625, and the I / O interface 620 are communicatively coupled by one or more interconnects 630. Additionally or alternatively, the computing device 600 represents a display device 107 associated with the user. Additionally or alternatively, as discussed above, the display device 107 can include the user's laptop, computer, smartphone, etc. In another embodiment, the computing device 600 is a server executed in a cloud environment.

[0218] In the illustrated embodiment, the storage 615 includes the user profile 118. The memory 610 includes a decision support engine 114 that itself includes the DAM 116. The decision support engine 114 is executed by the computing device 600 to perform the operations of the method 400 of FIG. 4 and / or the operations of the method 500 in FIG. 5.

[0219] As described above, the continuous analyte monitoring system 104 described in connection with FIG. 1 can be a multi-analyte sensor system that includes a multi-analyte sensor. FIGS. 7-11 depict exemplary multi-analyte sensors used to measure multiple analytes.

[0220] As used herein, the terms "analyte measurement device", "analyte monitoring device", "analyte sensing device", and / or "multianalyte sensor device" are broad terms and are given their ordinary and customary meaning to those of ordinary skill in the art (and are not limited to a special or customized meaning), and refer to, but are not limited to, devices and / or systems that are responsible for the detection of a particular analyte or combination of analytes or the conversion of signals associated therewith. For example, these terms can refer to, but are not limited to, devices responsible for the detection of a particular analyte or combination of analytes. In one embodiment, the device includes a sensor coupled to a circuit arranged within a housing and configured to process signals associated with analyte concentration into information. In one embodiment, such a device and / or system can use a biological recognition element combined with a conversion (detection) element to provide specific quantitative, semi-quantitative, qualitative, and / or semi-qualitative analytical information.

[0221] As used herein, the terms "biosensor" and / or "sensor" are broad terms and are given their ordinary and customary meaning to those of ordinary skill in the art (and are not limited to a special or customized meaning), and refer to, but are not limited to, a part of an analyte measurement device, analyte monitoring device, analyte sensing device, and / or multianalyte sensing device that is responsible for the detection of a particular analyte or combination of analytes or the conversion of signals 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 configured to form a surface that provides a signal during an electrochemical reaction. One or more membranes can be affixed to the body and can cover the electrochemically reactive surface. In one embodiment, such a biosensor and / or sensor can use a biological recognition element combined with a conversion (detection) element to provide specific quantitative, semi-quantitative, qualitative, semi-qualitative analytical signals.

[0222] As used herein, the terms "sensing moiety", "sensing membrane", and "sensing mechanism" are broad terms and are given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to a special or customized meaning), and refer to, but are not limited to, a biosensor and / or a part of a sensor that is responsible for the detection of a particular analyte or combination of analytes or the conversion of 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 sensing moiety, sensing membrane, and / or sensing mechanism can use a biological recognition element combined with a conversion (detection) element to provide specific quantitative, semi-quantitative, qualitative, semi-qualitative analytical information.

[0223] As used interchangeably herein, the terms "biomimetic membrane" and "biomimetic layer" are broad terms and are given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to a special or customized meaning), and refer to, but are not limited to, a permeable membrane (which can include multiple domains) or layer that functions as a biodefense interface between a host tissue and an implantable device. The terms "biomimetic interface" and "biodefense" are used interchangeably herein.

[0224] As used herein, the term "cofactor" is a broad term and is given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and non-limitingly refers to one or more substances whose presence contributes to or is required for the analyte-related activity of an enzyme, but is not limited thereto. Analyte-related activity can include, but is not limited to, any one or a combination of binding, electron transfer, and chemical conversion. Cofactors include coenzymes, non-protein chemical compounds, metal ions, and / or metal-organic complexes. Coenzymes include prosthetic molecular groups and co-substrates.

[0225] As used herein, the term "continuous" is a broad term and is given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to a portion, domain, coating, or layer that is uninterrupted or seamless, but is not limited thereto.

[0226] As used herein, the phrases "continuous analyte sensing" and "continuous multi - analyte sensing" are broad phrases and are given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to a special or customized meaning), and refer to a period during which monitoring of analyte concentration is carried out continuously, intermittently, and / or periodically (but regularly), for example, at intervals of about less than 1 second to about more than 1 week, but is not limited thereto. In further embodiments, the monitoring of analyte concentration is carried out at intervals of about 2, 3, 5, 7, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 seconds to about 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 analyte concentration is carried out at intervals of about 10, 20, 30, 40, or 50 minutes to about 1, 2, 3, 4, 5, 6, 7, or 8 hours. In further embodiments, the monitoring of analyte concentration is carried out at intervals of about 8 hours to about 12, 16, 20, or 24 hours. In further embodiments, the monitoring of analyte concentration is carried out at intervals of about 1 day to about 1.5, 2, 3, 4, 5, 6, or 7 days. In further embodiments, the monitoring of analyte concentration is carried out at intervals of about 1 week to about 1.5, 2, 3 weeks or more.

[0227] As used herein, the term "coaxial" should be broadly interpreted to include a sensor architecture having elements aligned along a common axis around a core that can be configured to have a circular, elliptical, triangular, polygonal, or other cross-section, such elements can include electrodes, insulating layers, or other elements that can be circumferentially positioned around a core layer such as a core electrode or a core polymer wire.

[0228] As used herein, the term "coupled" is a broad term and is given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to two or more system elements or components configured to be attached to at least one of electrical, mechanical, thermal, operable, chemical, or otherwise, but not limited thereto. For example, if an element is covalently, communicatively, electrostatically, thermally, mechanically, magnetically, or ionically associated with, or physically trapped, adsorbed, or absorbed by another element, the element is "coupled". Similarly, as used herein, the phrases "operably connected", "operably linked", and "operably coupled" may refer to one or more components coupled to another component in a manner that facilitates the transmission of at least one signal between the components. In some embodiments, the components are part of the same structure and / or integrated with each other, such as when they are covalently, electrostatically, mechanically, thermally, magnetically, ionically associated with, or physically trapped or absorbed (i.e., "directly coupled" as if there were no intervening elements). In other embodiments, the components are connected via remote means. For example, one or more electrodes can be used to detect an analyte 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 linked" to the electronic circuit. As used herein, the phrase "removably coupled" may refer to two or more system elements or components configured to be attached and removed electrically, mechanically, thermally, operably, chemically, or otherwise, without damaging any of the coupled elements or components, or so configured.As used herein, the phrase "permanently attached" refers to two or more system elements or components that are configured or attached to be electrically, mechanically, thermally, operably, chemically, or otherwise attached, but cannot be separated without damaging at least one of the attached elements or components, and are covalently, electrostatically, ionically associated, or physically trapped or absorbed.

[0229] As used herein, the term "discontinuous" is a broad term and is given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, a severed, interrupted, or separated portion, layer, coating, or domain.

[0230] As used herein, the term "distal" is a broad term and is given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, a region that is relatively far spaced from a reference point such as an origin or an attachment point.

[0231] As used herein, the term "domain" is a broad term and is given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, a region of a membrane system that can be a layer, a homogeneous or heterogeneous gradient (e.g., an anisotropic region of a membrane), or a portion of a membrane 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.

[0232] As used herein, the term "electrochemically reactive surface" is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, the surface of an electrode at which an electrochemical reaction occurs. In one example, this reaction is Faradaic and results in charge transfer between the surface and its environment. In one example, hydrogen peroxide produced by an enzyme-catalyzed reaction of an analyte oxidized on the surface results in a measurable electron current. For example, in the detection of glucose, glucose oxidase produces hydrogen peroxide (H2O2) as a byproduct. H2O2 reacts with the surface of the working electrode to produce two protons (2H + ), two electrons (2e - ), and one oxygen molecule (O2), which produces the detected electron current. At the counter electrode, a reducible species, such as O2, is reduced at the electrode surface to balance the current generated by the working electrode.

[0233] As used herein, the term "electrolysis" is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and non-limitingly refers to, but is not limited to, the electrochemical oxidation or reduction (collectively "redox") of a compound, either directly or indirectly, by one or more enzymes, cofactors, or mediators.

[0234] As used herein, the terms "implanted," "implanting," "embedded," or "implantable" are broad terms and are given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to a special or customized meaning), and refer to an object (e.g., a sensor) that is inserted or configured to be inserted subcutaneously (i.e., within the adipose layer between the skin and muscle), intradermally (i.e., through the stratum corneum and located within the epidermal or dermal layer of the skin), or transcutaneously (i.e., through, into, or across intact skin), which can result in a sensor having an in vivo portion and an ex vivo portion, but is not limited thereto. The term "implanted" also encompasses an object configured to be inserted subcutaneously, intradermally, or transcutaneously, whether or not it is itself inserted.

[0235] As used herein, the terms "interferent" and "interfering species" are broad terms and are given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to a special or customized meaning), and refer to an effect and / or species that interferes with the measurement of an analyte of interest in a sensor and produces a signal that does not accurately represent the analyte measurement, but is not limited thereto. In one example of an electrochemical sensor, an interfering species is a compound that produces a signal that is not analyte-specific due to a reaction on the electrochemically active surface.

[0236] As used herein, the term "in vivo" is a broad term and is given its ordinary and customary meaning to one of ordinary skill in the art (and are not limited to a special or customized meaning), and includes, but is not limited to, a portion of a device (e.g., a sensor) that is adapted for insertion into and / or presence within the body of a host.

[0237] As used herein, the term "ex vivo" is a broad term and is given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and includes, but is not limited to, a portion of a device (e.g., a sensor) adapted to remain and / or exist outside of the living body of the host.

[0238] As used herein, the terms and phrases "mediator" and "redox mediator" are broad terms and phrases and are given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to a special or customized meaning), and refer to any chemical compound or aggregate of compounds capable of directly or indirectly enabling electron transfer between an analyte, analyte precursor, analyte surrogate, analyte reducing enzyme or analyte oxidizing enzyme, or cofactor, and an electrode surface maintained at a potential, but are not limited thereto. In one example, the mediator accepts electrons from or transfers electrons to one or more enzymes or cofactors and / or exchanges electrons with the sensor system electrode. In one example, the mediator is a transition metal coordination organic molecule capable of reversible oxidation and reduction reactions. In other examples, the mediator can be an organic molecule or metal capable of reversible oxidation and reduction reactions.

[0239] As used herein, the term "membrane" is a broad term and is given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), serving as a protection for the exposed electrode surface from the biological environment, a diffusion resistance (limitation) for the analyte, a matrix for a catalyst (e.g., one or more enzymes) to enable an enzymatic reaction, a limitation or blocking of interfering species, providing hydrophilicity at the electrochemically reactive surface of the sensor interface, serving as an interface between the host tissue and the implanted device, modulating the host tissue response via drug (or other substance) release, and combinations thereof, but not limited thereto. Structures configured to perform functions including, but not limited to, those described above are referred to. As used herein, the terms "membrane" and "matrix" are meant to be interchangeable.

[0240] As used herein, the phrase "membrane system" is a broad phrase and is given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), which can be composed of two or more domains, layers, or layers within a domain, typically composed of a material with a thickness of several microns or more, permeable to oxygen, and optionally permeable to, for example, glucose or another analyte, but not limited thereto. In one embodiment, the membrane system includes an enzyme that enables an analyte reaction to occur, whereby the concentration of the analyte can be measured.

[0241] As used herein, the term "plane" should be broadly construed to describe a sensor architecture having a substrate that includes at least a first surface and an opposing second surface, and includes a plurality of elements disposed on one or more surfaces or edges of the substrate. The plurality of elements can include conductive layers or insulating layers or elements configured to operate as a circuit. The plurality of elements may or may not be electrically or otherwise coupled. In one embodiment, the plane includes one or more edges separating the opposing surfaces.

[0242] As used herein, the term "proximal" is a broad term and is given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a 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 the reference point and the enzyme domain is located closer to the sensor than the biointerface layer, the enzyme domain is more proximal to the sensor than the biointerface layer.

[0243] As used herein, the phrases "sensing portion", "sensing membrane", and "sensing mechanism" are broad phrases and are given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to a special or customized meaning), and refer to, but are not limited to, a biosensor and / or a part of a sensor that is responsible for the detection of a particular analyte or combination of analytes or the conversion of a signal associated therewith. In one embodiment, the sensing portion, 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 portion, sensing membrane, and / or sensing mechanism can use a biological recognition element combined with a conversion (detection) element to provide specific quantitative, semi-quantitative, qualitative, semi-qualitative analysis information.

[0244] During the general operation of an analyte measurement device, biosensor, sensor, sensing region, sensing portion, or sensing mechanism, a biological sample, such as blood or interstitial fluid, or components thereof, directly or after passing through one or more membranes, contacts an enzyme, such as glucose oxidase, DNA, RNA, or a protein or aptamer, such as one or more periplasmic binding proteins (PBPs) having one or more analyte binding regions or variants or fusion proteins thereof, and each region is capable of specifically or reversibly binding and / or reacting with at least one analyte. The interaction between the biological sample or its components and the analyte measurement device, biosensor, sensor, sensing region, sensing portion, or sensing mechanism results in a signal conversion that enables qualitative, semi-qualitative, quantitative, or semi-quantitative determination of analyte levels in the biological sample, such as glucose, ketones, lactate, potassium, etc.

[0245] In one embodiment, 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, such as a wire (coaxial) or conductive trace, or a substantially planar substrate comprising a substantially planar trace, and a membrane. In one embodiment, 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 an electrical connection at another location on the body, and a sensing membrane attached to the body and covering the electrochemically reactive surface. In some embodiments, the sensing membrane further comprises an enzyme domain, such as an enzyme domain, and an electrolyte phase, such as a free-flowing liquid phase comprising an electrolyte-containing fluid further described below. These terms are broad enough to include the entire device or only the sensing portion thereof (or something in between).

[0246] In another embodiment, the sensing region can include one or more peripheral membrane-binding proteins (PBPs) that contain the variant or fusion protein, or aptamers that have one or more analyte-binding regions, and each region can specifically and reversibly bind to at least one analyte. Changes in the aptamer or mutations in the PBP can contribute to or alter the long-term stability of the protein, including one or more binding constants, thermal stability, in order to bind the protein to a special encapsulation matrix, membrane or polymer, or to attach a detectable reporter group or "label" to indicate changes in the binding region, or to convert 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, changes in the hydrophobic / hydrophilic environmental change, three-dimensional conformational change, change 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 for the conversion of a detectable signal corresponding to one or more analytes present in the biological fluid.

[0247] In one embodiment, the sensing region determines the selectivity between one or more analytes such that only the analyte to be measured results in (converts) a detectable signal. This selection can be based on any chemical or physical recognition of the analyte by the sensing region, if the chemical composition of the analyte does not change, or if the sensing region causes or catalyzes an analyte reaction that changes the chemical composition of the analyte.

[0248] As used herein, the term "sensitivity" is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to the amount of signal (e.g., in the form of current and / or voltage) produced by a given quantity (unit) of the measured analyte, but is not limited thereto. For example, in one embodiment, the sensor has a sensitivity (or slope) of about 1 to about 100 picoamperes of current per mg / dL of analyte.

[0249] The terms "signal medium" or "transmission medium" should be construed to include any form such as a modulated data signal, a carrier wave, etc. The term "modulated data signal" means a signal in which one or more of its characteristics are set or changed so as to encode information in the signal.

[0250] As used herein, the terms "convert" or "conversion" and their grammatical equivalents are broad terms and are given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to a special or customized meaning), and refer to, but are not limited to, optical, electrical, electrochemical, acoustic / mechanical, or colorimetric techniques and methods. Electrochemical properties include current and / or voltage, inductance, capacitance, impedance, and potential. Optical properties include absorbance, fluorescence / phosphorescence, fluorescence / phosphorescence decay rate, wavelength shift, dual wave phase modulation, bioluminescence / chemiluminescence, reflectivity, light scattering, and refractive index. For example, the sensing region converts the recognition of an analyte into a semi - quantitative or quantitative signal.

[0251] As used herein, the term "transducing element" is a broad term when used herein and is given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, an analyte recognition moiety that can directly or indirectly facilitate detectable signaling 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 living cells, one or more oligonucleotides, and / or one or more DNA or RNA moieties. The transdermal continuous multi-analyte sensor can be used in vivo over various lengths of time. The continuous multi-analyte sensor system discussed herein can be a transdermal device in that a portion of the device can be inserted into the soft tissue below the host's skin while a portion of the device remains on the surface of the host's skin. In one aspect, to overcome short-term noise or problems associated with other sensor functions, one embodiment uses a material that promotes the formation of a fluid pocket around the sensor, such as a porous biocompatible interface membrane or matrix that creates a space between the sensor and the surrounding tissue. In some embodiments, the sensor comprises a spacer adapted to provide a fluid pocket between the sensor and the host tissue. This spacer, such as a biocompatible material, matrix, structure, etc. described in more detail elsewhere herein, is thought to provide oxygen and / or glucose transport to the sensor.

[0252] Membrane system The membrane systems disclosed herein are suitable for use in implantable devices that contact body fluids. For example, the membrane systems can be utilized with implantable devices for monitoring and determining analyte levels in body fluids, such as devices for monitoring glucose levels in individuals with diabetes. In some embodiments, the analyte measurement device is a continuous device. The analyte measurement device can use any suitable sensing element for providing a biological signal, including, but not limited to, those involving elements such as enzymes, chemistry, physics, electrochemistry, spectrophotometry, amperometry, potentiometry, polarimetry, calorimetry, radiometry, immunochemistry, etc.

[0253] Suitable membrane systems for the multi - analyte systems and devices described above can include, for example, the membrane systems disclosed in U.S. Patent No. 6,015,572, U.S. Patent No. 5,964,745, and U.S. Patent No. 6,083,523, the teachings of which are hereby incorporated herein by reference in their entirety.

[0254] Generally, the membrane system includes a plurality of domains, such as an electrode domain, an interference domain, an enzyme domain, a resistance domain, and a bio - interface domain. The membrane system can be deposited on an exposed electroactive surface using known thin - film techniques (e.g., evaporation, spraying, electrodeposition, dipping, brush coating, film coating, droplet coating, etc.). Additional steps can be applied, following the deposition of the membrane material, to improve specific properties such as mechanical properties, signal stability, and selectivity, for example, drying, annealing, and curing (e.g., UV curing, thermal curing, moisture curing, radiation curing, etc.). In a typical process, when depositing the resistance - domain membrane, a bio - interface / drug - release layer with 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 is formed. The “dry - film” thickness refers to the thickness of the cured film cast from the coating formulation by standard coating techniques.

[0255] In certain embodiments, the biointerface / drug release layer is formed from a biointerface polymer, which includes one or more membrane domains containing polyurethane and / or polyurea segments and one or more zwitterionic repeating units. In some embodiments, the biointerface / drug release layer coating is formed from a polyurethane urea having carboxybetaine groups and nonionic hydrophilic polyethylene oxide segments incorporated into the polymer. The polyurethane urea polymer is dissolved in an organic solvent system or a non-organic solvent system according to a predetermined coating formulation, crosslinked with an isocyanate crosslinking agent, and cured at a medium temperature of about 50 °C. The solvent system can be a single solvent or a mixture of solvents useful for dissolving or dispersing the polymer. The solvent can be one selected as the polymerization medium or can be added after the polymerization is complete. The solvent is selected from those having a lower boiling point to facilitate drying and be less toxic for implant applications. Examples of such 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 iterative steps of a selected process such as dipping to build the desired thickness. In still other embodiments, the bioprotective polymer is formed from a polyurethane urea having carboxylic acid groups and carboxybetaine groups incorporated into the polymer and a nonionic hydrophilic polyethylene oxide segment. The polyurethane urea polymer is dissolved in an organic solvent system or a non-organic solvent system in the coating formulation, crosslinked with a carbodiimide (e.g., 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC)), and cured at a medium temperature of about 50 °C.

[0256] In other embodiments, the biointerface / drug release layer coating is formed from a polyurethane urea having sulfobetaine groups and nonionic hydrophilic polyethylene oxide segments incorporated into the polymer, and the polyurethane urea polymer is dissolved in an organic or non-organic solvent system according to a predetermined coating formulation, crosslinked with an isocyanate crosslinking agent, and cured at a moderate temperature of about 50 °C. The solvent system can be a single solvent or a mixture of solvents useful for dissolving or dispersing the polymer. The solvent can be one selected as the polymerization medium or added after polymerization is complete. The solvent is selected from those having a lower boiling point to facilitate drying and 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 percent of polymer solids), the coating can be applied in a single step or multiple iterative steps of a selected process such as dipping to build the desired thickness. In still other embodiments, the biointerface polymer is formed from a polyurethane urea having unsaturated hydrocarbon groups and sulfobetaine groups incorporated into the polymer, as well as nonionic hydrophilic polyethylene oxide segments, and the polyurethane urea polymer is dissolved in an organic or non-organic solvent system in the coating formulation and crosslinked in the presence of an initiator by irradiation including heat or UV, LED light, electron beam, etc., and cured at a moderate temperature of about 50 °C. Examples of unsaturated hydrocarbons include allyl groups, vinyl groups, acrylates, methacrylates, alkenes, alkynes, and the like.

[0257] In some embodiments, a tether is used. The tether is a polymer or chemical moiety that does not participate in the (electro)chemical reaction involved in sensing but forms a chemical bond with the (electro)chemical active component of the membrane. In some embodiments, these bonds are covalent bonds. In one embodiment, the tether can be formed in solution before one or more intermediate layers of the membrane are formed, and the tether either directly binds two (electro)chemical active components to each other or, alternatively, the tether binds the (electro)chemical active component to a polymer backbone structure. In another embodiment, the (electro)chemical active components are mixed with a crosslinking agent (and optionally a polymer) having an adjustable length, and the tethering reaction occurs as an in-situ crosslinking. Tethering can be used to maintain a predetermined degree 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.

[0258] Membrane Fabrication The polymer can be processed by solution-based techniques such as, for example, spraying, dipping, casting, electrospinning, vapor deposition, spin coating, coating, etc. Aqueous polymer emulsions can be manufactured to form membranes in a manner similar to that used for solvent-based materials. In both cases, the evaporation of the volatile liquid (e.g., an 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 many methods. The liquid system can be cured by heat, moisture, high-energy radiation, ultraviolet light, or by completing the reaction, which produces the final polymer in a mold or on the substrate to be coated.

[0259] In some embodiments, the wetting characteristics of the film (and, by extension, the degree of sensor drift indicated by the sensor) can be adjusted and / or controlled by creating covalent crosslinks between a surface-active group-containing polymer, a functional group-containing polymer, a polymer having zwitterionic groups (or a precursor or derivative thereof), and combinations thereof. The crosslinking can have a substantial effect on the film structure, which can in turn affect the surface wetting characteristics of the film. The crosslinking can also affect the tensile strength, mechanical strength, water absorption rate, and other properties of the film.

[0260] Crosslinked polymers can have different crosslink densities. In certain embodiments, crosslinking agents are used to facilitate crosslinking between layers. In other embodiments, instead of (or in addition to) the crosslinking techniques described above, heat is used to form the crosslinks. For example, in some embodiments, imide bonds and amide bonds can be formed between two polymers as a result of high temperature. In some embodiments, photo-crosslinking is performed to form covalent bonds between the polycation layer and the polyanion layer. One major advantage of photo-crosslinking is that it provides the possibility of patterning. In certain embodiments, patterning using photo-crosslinking is performed to modify the film structure and, thus, to adjust the wetting characteristics of the film and the membrane system, as discussed herein.

[0261] Polymers having domains or segments functionalized to enable crosslinking can be made by at least the methods contemplated herein. For example, a polyurethane urea polymer having an aromatic segment or an aliphatic segment with an electrophilic functional group (e.g., a carbonyl group, an aldehyde group, an anhydride group, an ester group, an amide group, an isocyano group, an epoxy group, an allyl group, or a halo group) can be crosslinked with a crosslinking agent having a plurality of nucleophilic groups (e.g., a hydroxyl group, an amine group, a urea group, a urethane group, or a thiol group). In a further embodiment, a polyurethane urea polymer having an aromatic segment or an aliphatic segment with a nucleophilic functional group can be crosslinked with a crosslinking agent having a plurality of electrophilic groups. Further, a polyurethane urea polymer having a hydrophilic segment with a nucleophilic functional group or an electrophilic functional group can be crosslinked with a crosslinking agent having a plurality of electrophilic groups or nucleophilic groups. Unsaturated functional groups on the polyurethane urea can also be used for crosslinking by reacting with a polyvalent free radical agent. Non-limiting examples of suitable crosslinking agents include isocyanates, carbodiimides, glutaraldehyde, aziridines, silanes, or other aldehydes, epoxies, acrylates, free radical-based agents, ethylene glycol diglycidyl ether (EGDE), poly(ethylene glycol) diglycidyl ether (PEGDE), or dicumyl peroxide (DCP). In one embodiment, about 0.1% to about 15% w / w of the crosslinking agent is added relative to the total dry weight of the crosslinking agent and polymer added when blending the components. In another embodiment, about 1% to about 10% w / w of the crosslinking agent is added relative to the total dry weight of the crosslinking agent and polymer added when blending the components. In yet another embodiment, about 5% to about 15% w / w of the crosslinking agent is added relative to the total dry weight of the crosslinking agent and polymer added when blending the components. During the curing process, it is believed that substantially all of the crosslinking agent reacts, leaving substantially no detectable unreacted crosslinking agent in the final film.

[0262] The polymers disclosed in this specification can be formulated into mixtures that can be stretched into films or applied to surfaces using methods such as self-assembling monolayers (SAMs), spraying, coating, dip coating, vapor deposition, molding, 3D printing, lithography techniques (e.g., photolithography), micro- and nanopipetting printing techniques, silk screen printing, and the like. The mixture can then be cured at elevated temperatures (e.g., from about 30 °C to about 150 °C). Other suitable curing methods can include, for example, ultraviolet light, electron beam, or gamma rays.

[0263] In some situations, using a continuous multi-analyte monitoring system that includes sensors composed of a bioprotective membrane and / or a drug release membrane, it is thought that the foreign body reaction can be managed or manipulated such that it is a major event surrounding the extended implantation of the implanted device and supports rather than obstructs or blocks analyte transport. In another aspect, to extend the lifespan of the sensor, one example uses materials that promote intussusceptive angiogenesis within, for example, a porous biologic interface membrane. For example, intussusceptive growth within the porous biologic interface material surrounding the sensor can promote sensor function over a long period (e.g., weeks, months, or years). It has been observed that intussusceptive growth and formation of the tissue bed can take up to 3 weeks. Intussusceptive growth and tissue bed formation are thought to be part of the foreign body reaction. As discussed herein, the foreign body reaction can be manipulated by the use of porous bioprotective materials that surround the sensor and promote intussusceptive growth of tissue and the microvasculature over time.

[0264] Thus, sensors as contemplated in the examples herein can include a biologic interface layer. The biologic interface layer can include, but is not limited to, a porous biologic interface material that includes, for example, a solid portion and interconnected voids, as with the drug release layer, all of which are described in more detail elsewhere herein. The biologic interface layer can be used to improve sensor function over the long term (e.g., after intussusceptive growth of tissue).

[0265] Accordingly, a sensor as contemplated in the examples herein may include a drug release membrane that functions at least in part as a biological interface membrane or in combination with a biological interface membrane. The drug release membrane may include, for example, a material that includes a hard-soft segment polymer having a hydrophilic domain and optionally a hydrophobic domain, all of which are described in more detail elsewhere herein and can be used to improve sensor functionality over a long period of time (e.g., after in-growth into tissue). In one example, a material that includes a hard-soft segment polymer having a hydrophilic domain and optionally a hydrophobic domain is configured to release a combination of a dexamethasone or a dexamethasone acetate derivative form and dexamethasone such that one or more different release rates of an anti-inflammatory drug are achieved and the useful life of the sensor is extended. Other suitable drug release membranes of the present disclosure include silicone polymers, polytetrafluoroethylene, expanded polytetrafluoroethylene, ethylene tetrafluoroethylene copolymer, polyolefins, polyesters, polycarbonates, biostable polytetrafluoroethylene, homopolymers, copolymers, terpolymers of polyurethanes, 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 etherketone, PEEK), polyamide, polyurethane and copolymers and blends thereof, polyurethane urea 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, polysulfone and block copolymers (e.g., including diblock, triblock, alternating, random and graft copolymer cellulose), hydrogel polymers, poly(2-hydroxyethyl methacrylate, pHEMA (poly(2-hydroxyethyl methacrylate)) and copolymers and blends thereof, hydroxyethyl methacrylate (hydroxyethyl methacrylate, HEMA) and copolymers and blends thereof, polyacrylonitrile-polyvinyl chloride (polyacrylonitrile-polyvinyl chloride, PAN-PVC) and copolymers and blends thereof, acrylic copolymers and copolymers and blends thereof, nylon and copolymers and blends thereof, polyvinylidene fluoride, polyanhydrides, poly(l-lysine), poly(L-lactic acid), pans and copolymers and blends thereof, hydroxyapatite and copolymers and blends thereof.

[0266] Exemplary multi-analyte sensor membrane configurations Continuous multi-analyte sensors are provided that have various membrane configurations suitable for facilitating signal conversion corresponding to analyte concentration simultaneously, intermittently, and / or sequentially. In one embodiment, such a sensor can be constructed using a signal transducer comprising one or more transducing elements ("TL"). Such continuous multi-analyte sensors can use various transduction means, e.g., among other techniques, amperometric measurements, coulometric measurements, potentiometric measurements, and impedance measurement methods.

[0267] In one embodiment, the conversion element includes one or more membranes that can include one or more layers and / or domains, and each of the one or more layers and / or domains can independently include one or more signal converters, such as enzymes, RNA, DNA, aptamers, binding proteins, and the like. As used herein, the conversion element includes enzymes, ionophores, RNA, DNA, aptamers, binding proteins, and is used interchangeably.

[0268] In one embodiment, the conversion element is present within one or more membranes, layers, or domains formed over the sensing region. In one embodiment, such a sensor can be constructed using a membrane domain that includes one or more enzyme domains, such as an enzyme domain also referred to as an EZ layer (“EZL”), and each enzyme domain can include one or more enzymes. References to the following “enzyme layer” are intended to include all or part of the enzyme domain, any of which can be all or part of a membrane system as contemplated herein, for example, as a single layer, as two or more layers, as a pair of bilayer, or as a combination thereof.

[0269] In one embodiment, the continuous multi - analyte sensor uses one or more of the following analyte - substrate / enzyme pairs, for example, sarcosine oxidase in combination with creatinine amidohydrolase, the creatinine amidohydrolase used for sensing creatinine. Other examples of analyte / oxidase enzyme combinations that can be used in the sensing region are, for example, alcohol / alcohol oxidase, cholesterol / cholesterol oxidase, 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 can be used including analyte - substrate / enzyme pairs that include recombinant enzymes, immobilized enzymes, mediator - wired enzymes, dimerizing enzymes and / or fusion enzymes.

[0270] NAD - based multi - analyte sensor platform Nicotinamide adenine dinucleotide (NAD(P) + / NAD(P)H) is 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, for example, the oxidized form (NAD(P)+) and the reduced form (NAD(P)H) (H = hydrogen). The reaction between NAD+ and NADH is reversible, and thus the coenzyme can continuously cycle between the NAD(P) + / form and the NAD(P)H form without being essentially consumed.

[0271] In one embodiment, one or more enzyme domains of the sensing region of the continuous multi - analyte sensor device of the present disclosure include an amount of NAD+ or NADH to provide conversion of a detectable signal corresponding to the presence or concentration of one or more analytes. In one embodiment, one or more enzyme domains of the sensing region of the continuous multi - analyte sensor device of the present disclosure include an excess amount of NAD+ or NADH to provide an enhanced conversion of a detectable signal corresponding to the presence or concentration of one or more analytes.

[0272] In one embodiment, NAD, NADH, NAD+, NAD(P)+, ATP, flavin adenine dinucleotide (FAD), magnesium (Mg++), pyrroloquinoline quinone (PQQ), and their functionalized derivatives can 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 their functionalized derivatives are incorporated into the sensing region. In one embodiment, NAD, NADH, NAD+, NAD(P)+, ATP, flavin adenine dinucleotide (FAD), magnesium (Mg++), pyrroloquinoline quinone (PQQ), and their functionalized derivatives are dispersed or distributed in one or more membranes or domains of the sensing region.

[0273] In one aspect of the present disclosure, the sequential 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 example, the membrane or domain provides for the retention and stable reuse of NAD+ and a mechanism for converting NADH oxidation or NAD+ reduction into a current measurable by amperometry. In one example described below, a sequential sensing of multiple analytes is provided, where one or more of them are reversibly bound or at least one of them is oxidized or reduced by a NAD+-dependent enzyme, such as ketone (beta-hydroxybutyrate dehydrogenase), glycerol (glycerol dehydrogenase), cortisol (11β-hydroxysteroid dehydrogenase), glucose (glucose dehydrogenase), alcohol (alcohol dehydrogenase), aldehyde (aldehyde dehydrogenase), and lactate (lactate dehydrogenase). In other examples described below, membranes are provided that enable the sequential on-body sensing of multiple analytes utilizing FAD-dependent dehydrogenases, such as fatty acid (acyl-CoA dehydrogenase).

[0274] Provided are exemplary configurations of one or more membranes or portions thereof that provide for the retention and reuse of NAD⁺. Thus, the electrode surface of a conductive wire (coaxial) or a planar conductive surface is coated with at least one layer containing at least one enzyme, as depicted in FIG. 7A. Referring to FIG. 7B, one or more optional layers can be positioned between the electrode surface and one or more enzyme domains. For example, one or more interference domains (also referred to as an "interferent blocking layer") can be used to reduce or eliminate signal contributions from unwanted species present, or one or more electrodes (not shown) can be used to assist with wetting, system equilibration, and / or startup. As shown in FIGS. 7A-7B, the 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 positions can be switched to facilitate better consumption of excess NAD⁺ and slower unwanted outward diffusion. Exemplary sensor configurations can be found in U.S. Provisional Patent Application No. 63 / 321340, "CONTINUOUS ANALYTE MONITORING SENSOR SYSTEMS AND METHODS OF USING THE SAME," filed on March 18, 2022, which is hereby incorporated by reference in its entirety.

[0275] In one embodiment, one or more mediators optimal for NADH oxidation are incorporated into one or more electrode domains or enzyme domains. In one embodiment, organic mediators such as phenanthroline dione 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, for example, polyvinylimidazole-Os(bpy)2Cl, or polyvinylpyridine-organometallic coordination mediator (including ruthenium-phenanthroline dione) are used. Other mediators can be used as further discussed below.

[0276] In humans, the serum levels of beta-hydroxybutyrate (BHB) are normally in the low micromolar range but can increase up to about 6 - 8 mM. The serum levels of BHB can reach 1 - 2 mM after intense exercise or inconsistent levels above 2 mM are achieved by a carbohydrate-free ketogenic diet. Other ketones such as acetoacetate and acetone are present in the serum, but most of the dynamic range in ketone levels is in the form of BHB. Therefore, monitoring of BHB, for example, continuous monitoring, is useful for providing health information to the user or healthcare provider.

[0277] Another embodiment of a continuous ketone analyte detection configuration using an electrode-related mediator-bound diaphorase / NAD+ / dehydrogenase is depicted below.

[0278]

Chemical formula

[0279] In one embodiment, the diaphorase is electrically coupled to an electrode having an organometallic coordination mediator polymer. In another embodiment, the diaphorase is covalently coupled to an electrode having an organometallic coordination mediator polymer. Alternatively, multiple enzyme domains can be used in the enzyme layer to essentially separate NADH oxidation from analyte (ketone) oxidation, for example, separating an electrode-associated diaphorase (closest to the electrode surface) from more distal adjacent NAD+ or dehydrogenase enzymes. Alternatively, NAD+ can be closer to the electrode surface than an adjacent enzyme domain containing a dehydrogenase enzyme. In one embodiment, NAD+ and / or HBDH are present in the same or different enzyme domains, and both can be immobilized using, for example, an amine-reactive crosslinking agent (e.g., glutaraldehyde, epoxide, NHS ester, imide ester). In one embodiment, NAD+ is bound 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 crosslinking agent. In one embodiment, NAD+ can be covalently coupled to a form of an enzyme domain having 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.

[0280] In one embodiment, the sensing region includes one or more NADH, acceptor oxidoreductase, and one or more NAD-dependent dehydrogenases. In one embodiment, the sensing region includes one or more NADH, acceptor oxidoreductase, 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 includes an amount of diaphorase.

[0281] In one embodiment, a ketone sensing configuration suitable for combination with another analyte sensing configuration is provided. Thus, an EZL layer about 1 to 20 μm thick is prepared by providing an EZL solution composition in 10 mM HEPES in water having about 20 μL of 500 mg / mL HBDH, about 20 μL of [500 mg / mL NAD(P)H, about 200 mg / mL polyethylene glycol - diglycol ether (PEG - DGE) of about 400 MW], about 20 μL of 500 mg / mL diaphorase, and adding about 40 μL of 250 mg / mL polyvinylimidazole - osmium bis(2,2'-bipyridine) chloride (PVI - Os(bpy)2Cl) to a substrate such as a working electrode and, after drying, providing about 15 to 40 wt% HBDH, about 5 to 30% diaphorase, about 5 to 30% NAD(P)H, about 10 to 50% PVI - Os(bpy)2Cl, and about 1 to 12% PEG - DGE (400 MW). Substrates contemplated herein that may include the working electrode are gold, platinum, palladium, rhodium, iridium, titanium, tantalum, chromium, and / or their alloys or combinations, or carbon (e.g., graphite, vitreous carbon, carbon nanotubes, graphene, or doped diamond, and combinations thereof can be formed.

[0282] The above enzyme domain was contacted with a resistance domain, also referred to as a resistance layer ("RL"). In one embodiment, the RL comprises about 55 to 100% PVP and about 0.1 to 45% PEG - DGE. In another embodiment, the RL comprises about 75 to 100% PVP and about 0.3 to 25% PEG - DGE. In yet another embodiment, the RL comprises about 85 to 100% PVP and about 0.5 to 15% PEG - DGE. In yet another embodiment, the RL consists essentially of 100% PVP.

[0283] The exemplary continuous ketone sensor depicted in FIGS. 7A-7B that includes a NAD(P)H reservoir domain is configured such that NAD(P)H is not rate-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 "membrane") may also contain a polymer or protein binder 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.), such that, optionally, the NAD(P)H reservoir membrane also provides a catalytic function. In one embodiment, NAD(P)H is dispersed or distributed within or with a polymer (or protein) and crosslinked to such an extent that appropriate enzyme / cofactor function and / or reduced NAD(P)H flux within the domain is still possible.

[0284] In one embodiment, the NADH oxidase enzyme is used in one or more membranes of the sensing region, either alone or in combination with superoxide dismutase (SOD). In one embodiment, an amount of superoxide dismutase (SOD) capable of scavenging some or most of the one or more free radicals generated by NADH oxidase is used. In one embodiment, the NADH oxidase enzyme is used in combination with a functionalized polymer having NAD(P)H immobilized on the polymer from NAD(P)H and / or the C6-terminal amine in one or more membranes of the sensing region, either alone or in combination with superoxide dismutase (SOD).

[0285] In one embodiment, NAD(P)H is immobilized to an extent that maintains its NAD(P)H catalytic function. In one embodiment, dimeric 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.

[0286] The continuous ketone sensor configuration described above can be adapted for other analytes or used in combination with other sensor configurations. For example, an analyte-dehydrogenase enzyme combination can be used in any of the membranes of the sensing region including glycerol (glycerol dehydrogenase), cortisol (11β-hydroxysteroid dehydrogenase), glucose (glucose dehydrogenase), alcohol (alcohol dehydrogenase), aldehyde (aldehyde dehydrogenase), and lactate (lactate dehydrogenase).

[0287] In one embodiment, the semipermeable membrane is used in the sensing region, adjacent to it, or adjacent to one or more membranes of the sensing region so as to attenuate the flow of at least one analyte or chemical species. In one embodiment, the semipermeable membrane attenuates the flow of at least one analyte or chemical species so as to provide a linear response from the converted signal. In another embodiment, the semipermeable membrane prevents or precludes the outflow of NAD(P)H from the sensing region or any membrane or domain. In one embodiment, the semipermeable membrane can be an ion-selective membrane selective for ionic analytes of interest such as ammonium ions.

[0288] In another example, a continuous multi - analyte sensor configuration including one or more enzymes and / or at least one cofactor was prepared. FIG. 7C depicts this exemplary configuration of an enzyme domain 750 that includes an enzyme with an amount of cofactor (Enzyme) located proximate to at least a portion of the surface of a working electrode (WE), where the WE includes an electrochemically reactive surface. In one example, a second membrane 751 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 the sensor lifetime. One or more resistance domains 752 (“RL”) are located adjacent to the second membrane (or can be between the membranes). The RL can be configured to block the diffusion of cofactor from the second membrane. The electron transfer from the cofactor to the WE converts a signal that directly or indirectly corresponds to the analyte concentration.

[0289] FIG. 7D depicts an alternative enzyme domain configuration that includes a first membrane 751 having an amount of cofactor located more proximally to at least a portion of the WE surface. An enzyme domain 750 that includes an amount of enzyme is located adjacent to the first membrane.

[0290] In the membrane configurations depicted in FIGS. 7C - 7D, the generation of electrochemically active species in the enzyme domain diffuses to the WE surface and converts a signal that directly or indirectly corresponds to the analyte concentration. In some examples, the electrochemically active species includes hydrogen peroxide. In sensor configurations that include cofactors, the cofactor from the first layer can diffuse into the enzyme domain to extend the sensor lifetime, for example, by regenerating the cofactor. In other sensor configurations, cofactors can optionally be included to improve performance attributes such as stability. For example, a continuous ketone sensor can include NAD(P)H and Mg +2It can contain divalent metal cations such as etc. One or more resistance domains RL can be located adjacent to the second membrane (or can be in between layers). RL can be configured to block the diffusion of cofactors and / or interfering substances from the second membrane from reaching the WE surface. Other components such as electrodes, resistors, biointerfaces, and drug release membranes, layers, or domains can be used in the aforementioned configurations. In other embodiments, the continuous analyte sensor contains one or more cofactors that contribute to sensor performance.

[0291] Figure 7E depicts another continuous multi-analyte membrane configuration where beta-hydroxybutyrate dehydrogenase BHBDH in the first domain 753 is located close to the working electrode WE, and a second domain 754 containing, for example, alcohol dehydrogenase (ADH) and NADH is located adjacent to the first domain. One or more resistance domains RL752 can be deployed adjacent to the second enzyme domain 754. In this configuration, the presence of a combination of alcohol and ketone in serum serves to collectively provide a converted signal corresponding to at least one of the analyte concentrations, for example, the ketone. Thus, as NADH present in the more distal second enzyme domain consumes the alcohol present in the serum environment, NADH is oxidized to NAD(P)H and diffuses into the first membrane layer, providing an electron transfer catalyzed by BHBDH of acetoacetate ketone and a conversion of the detection signal corresponding to the concentration of the ketone. In one embodiment, the enzyme can be configured for reverse catalysis and can create substrates for the catalysis of another enzyme present in either the same or a different layer or domain. Other components such as electrodes, resistors, biointerfaces, and drug release membranes, layers, or domains can be used in the aforementioned configurations. Thus, the first enzyme domain that is more distal from the WE than the second enzyme domain can be configured to generate cofactors or other elements for the second enzyme domain to act as reactants (and / or reactant substrates) for detecting one or more target analytes.

[0292] Alcohol sensor configuration In one embodiment, a continuous alcohol (e.g., ethanol) sensor device configuration is provided. In one embodiment, one or more enzyme domains including alcohol oxidase (AOX) are provided, and the presence and / or amount of alcohol is converted, either alone or in combination with an oxygen consumption or another substrate-oxidase enzyme system, such as glucose-glucose oxidase, by the creation of hydrogen peroxide, and the hydrogen peroxide and / or oxygen and / or glucose can be detected and / or measured qualitatively or quantitatively using amperometric measurements.

[0293] In one embodiment, the sensing region for the aforementioned enzyme substrate-oxidase enzyme configuration has one or more enzyme domains including one or more electrodes. In one embodiment, the sensing region for the aforementioned enzyme substrate-oxidase enzyme configuration has one or more enzyme domains with or without one or more electrodes, and further comprises one or more interference blocking membranes (e.g., a selective permeable membrane, a charge exclusion membrane) to attenuate the diffusion of one or more interfering substances through the membrane to the working electrode. In one embodiment, the sensing region for the aforementioned substrate-oxidase enzyme configuration has 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 blocking membranes to attenuate one or more analytes or enzyme substrates. In one embodiment, the sensing region for the aforementioned substrate-oxidase enzyme configuration has one or more enzyme domains with or without one or more electrodes, and one or more resistance domains with or without one or more interference blocking membranes further comprise, independently, one or more biocompatible membranes and / or drug release membranes to attenuate one or more analytes or enzyme substrates and to attenuate the immune response of the host after insertion.

[0294] In one embodiment, one or more interference blocking films are deposited adjacent to the working electrode and / or the electrode surface. In one embodiment, one or more interference blocking films are deposited directly adjacent to the working electrode and / or the 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 the electrode surface, attenuating one or all analytes that diffuse through the sensing region, excluding 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 when diffusing to the working electrode.

[0295] In one embodiment, the working electrode used included platinum and the applied potential was about 0.5 volts.

[0296] In one embodiment, electrochemically sensing changes in oxygen levels can be performed, for example, in a Clark-type electrode configuration or, in a different configuration, by coating the electrodes with one or more films of one or more polymers such as NAFION™. Based on the change in potential, the change in oxygen concentration can be recorded, which is directly or indirectly correlated with the concentration of alcohol. When appropriately designed to follow stoichiometric behavior, the presence of a specific concentration of alcohol should cause a corresponding reduction in local oxygen in direct (linear) relation to the concentration of alcohol. Thus, a multi-analyte sensor for both alcohol and oxygen can thus be provided.

[0297] In another embodiment, the alcohol sensing configuration described above can include one or more secondary enzymes that react with the reaction products of alcohol / alcohol oxidase catalysis, such as hydrogen peroxide, and can 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 peroxidase, such as horseradish peroxidase. The alcohol / alcohol oxidase can be in the same or a different layer as the peroxidase, or they can be spatially separated distally from the electrode surface, for example, 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 to the electrode surface and the peroxidase is more distal from the electrode surface. In one embodiment, the alcohol / alcohol oxidase that is more distal from the electrode surface and the peroxidase further includes any combination of electrodes, interference, resistance, and biocompatible membranes to optimize the signal, durability, reduce drift, or extend the end of the useful life.

[0298] 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 the electrode surface and / or are deposited on or otherwise associated with the surface of the working electrode (WE) or the reference electrode (RE). In one embodiment, the one or more mediators eliminate or reduce the direct oxidation of interfering species that can reach the WE or RE. In one embodiment, the one or more mediators can lower the operating potential of the WE / RE, for example, to about 0.6 V to about 0.3 V or less on a platinum electrode, thereby reducing or eliminating the oxidation of endogenous interfering species. Examples of one or more mediators are provided below. Other electrodes, such as counter electrodes, can be used.

[0299] In one embodiment, other enzymes or additional components may be added to the polymer mixture that constitutes any part 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. The 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 increasing shelf life and / or operational stability and thus may desirably be reduced or removed. In one embodiment, xanthine oxidase may be used to remove by-products of one or more enzyme reactions.

[0300] In another embodiment, dehydrogenase enzymes are used with oxidases for the detection of alcohol alone or in combination with oxygen. Thus, in one embodiment, alcohol dehydrogenase is used to oxidize alcohol to aldehyde in the presence of reduced nicotinamide adenine dinucleotide (NAD(P)H) or nicotinamide adenine dinucleotide phosphate (NAD(P)+). To provide a continuous source of NAD(P)H or NAD(P)+, NADH oxidase or NADPH oxidase is used to oxidize NAD(P)H or NAD(P)+ while consuming oxygen. In another embodiment, diaphorase may be used instead of or in combination with NADH oxidase or 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 adapted to the intended duration of the planned lifetime of the sensor.

[0301] In the foregoing dual-enzyme configuration, the signal can be sensed either by (1) an electrically coupled (e.g., "wired") alcohol dehydrogenase (ADH) using, for example, an electroactive hydrogel polymer containing one or more mediators, or (2) oxygen electrochemistry sensing for measuring 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 in the enzyme domain together 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, interference, resistance, and biocompatible membranes can be used to optimize the signal, durability, reduce drift, or extend the end of the useful life. In one embodiment, for example, at least a portion of a transduction element such as an aptamer, enzyme, or cofactor, and a direct or indirect electrical coupling via a covalent or ionic bond to at least a portion of the electrode surface is provided. A chemical moiety capable of assisting electron transfer from the enzyme or cofactor to the electrode surface can be used and includes one or more mediators as described below.

[0302] In one embodiment, any one of the foregoing continuous alcohol sensor configurations is combined with any one of the foregoing continuous ketone monitoring configurations to provide a continuous multi-analyte sensor device as further described below. In one embodiment, the continuous glucose monitoring configuration is combined with any one of the foregoing continuous alcohol sensor configurations and any one of the foregoing continuous ketone monitoring configurations to provide a continuous multi-analyte sensor device as further described below.

[0303] 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 can be positioned adjacent to the working electrode surface. The catalytic action of uric acid using UOX produces hydrogen peroxide that can be detected using amperometric, electrolytic coulometric, and impedance measurement methods, among other techniques. In one embodiment, to reduce or eliminate interference from the direct oxidation of uric acid on the electrode surface, one or more electrodes, 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 the diffusion of uric acid and other analytes that can potentially interfere with signal transduction to the working electrode.

[0304] In an alternative embodiment, the continuous uric acid sensing device configuration includes sensing changes in oxygen levels around the WE surface, such as in a Clark-type electrode setup, or one or more electrodes can independently include one or more different polymers, such as NAFION™, a polyzwitterionic polymer, or a polymeric mediator adjacent to at least a portion of the electrode surface. In one embodiment, the electrode surface having one or more electrode domains provides operation at different voltages or lower voltages for measuring 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.

[0305] In one embodiment, one or more coatings may be deposited on the WE surface to lower the potential at the WE for the signal transduction of uric acid, either alone or in combination with any of the foregoing configurations, the uric acid sensor configuration. The one or more coatings may be deposited or otherwise formed on the WE surface and / or on other coatings formed thereon using various techniques including, but not limited to, dipping, electrodeposition, vapor deposition, spray coating, etc. In one embodiment, the coated WE surface can provide an oxidation-reduction reaction of hydrogen peroxide, for example, at a lower potential (compared to 0.6V on the 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.

[0306] In one embodiment, one or more secondary enzymes, cofactors, and / or mediators (electrically coupled mediators or polymeric mediators) may be added to the enzyme domain having UOX to facilitate direct or indirect electron transfer to the WE. In such a configuration, for example, the regeneration of the first 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).

[0307] Choline sensor configuration In one example, a continuous choline sensor device can be provided using, for example, a choline oxidase enzyme that produces hydrogen peroxide by the oxidation of choline. Thus, in one example, 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 films located between the WE surface and the at least one enzyme domain. The catalysis of choline using COX results in the creation of hydrogen peroxide that can be detected, among other techniques, using amperometric, electrolytic coulometric, and impedance measurement methods.

[0308] In one example, the continuous choline sensor configuration described above is combined with either the continuous alcohol sensor configuration or the continuous uric acid sensor configuration described further below to provide a continuous multi-analyte sensor device. This continuous multi-analyte sensor device can further include continuous glucose monitoring capabilities. Other membranes can be used in the continuous choline sensor configuration described above, such as electrodes, resistors, biocompatible interfaces, and drug release membranes.

[0309] Cholesterol sensor configuration In one example, a continuous cholesterol sensor configuration can be fabricated using cholesterol oxidase (CHOX), similar to the sensors described above. Thus, one or more enzyme domains containing CHOX can be located adjacent to at least one WE surface. The catalysis of free cholesterol using CHOX results in the creation of hydrogen peroxide that can be detected, among other techniques, using amperometric, electrolytic coulometric, and impedance measurement methods.

[0310] An exemplary cholesterol sensor configuration using a platinum WE was prepared in which at least one interference film is located adjacent to at least one WE surface, on which is present at least one enzyme domain containing CHOX, and on which is located at least one resistance domain for controlling diffusion characteristics.

[0311] 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. Since cholesterol has low solubility in water, a significant amount of cholesterol is in unmodified and esterified forms, so measuring different types of cholesterol and total cholesterol is important. Thus, in one embodiment, for example, a total cholesterol sample in which a secondary enzyme is introduced into at least one enzyme domain is provided 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 the esterase.

[0312] In one embodiment, the aforementioned continuous (total) cholesterol sensor configuration is combined with any one of the aforementioned continuous alcohol sensor configuration and / or continuous uric acid sensor configuration to provide a continuous multi-analyte sensor system as further described below. This continuous multi-analyte sensor device can further include continuous glucose monitoring capabilities. Other membrane configurations can be used in the aforementioned continuous cholesterol sensor configuration, such as one or more electrode domains, resistance domains, biocompatible domains, and drug release membranes.

[0313] Configurations 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 final product of the catalytic action of bilirubin oxidase and ascorbic acid oxidase on the analyte is water instead of hydrogen peroxide. Therefore, hydrogen peroxide redox detection for correlating 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 the target analyte present. Therefore, bilirubin and ascorbic acid levels can be measured indirectly by electrochemically sensing changes in oxygen levels, for example, as in a Clark-type electrode setup.

[0314] Alternatively, different configurations for sensing bilirubin and ascorbic acid can be used. For example, an electrode domain comprising one or more electrode domains containing an electron transfer agent such as NAFION™, a polyzwitterionic polymer, or a polymeric mediator can be coated onto the electrode. The measured oxygen levels converted from such enzyme domain configurations can be correlated with bilirubin concentration and ascorbic acid levels. In one embodiment, an electrode domain comprising one or more mediators electrically coupled to a working electrode can be used and correlated with bilirubin levels and ascorbic acid levels.

[0315] In one embodiment, the foregoing continuous bilirubin and ascorbic acid sensor configurations can be combined with any one of the foregoing continuous alcohol sensor configuration, continuous uric acid sensor configuration, continuous cholesterol sensor configuration to provide a continuous multi-analyte sensor device as further described below. This continuous multi-analyte sensor device can further include continuous glucose monitoring capabilities. Other membranes can be used in the foregoing continuous bilirubin and ascorbic acid sensor configurations, such as electrodes, resistors, biocompatible interfaces, and drug release membranes.

[0316] One working electrode configuration for dual analyte detection In one embodiment, there is provided at least a dual enzyme domain configuration in which each layer contains one or more specific enzymes and optionally one or more cofactors. Broadly, one embodiment of a continuous multi - analyte sensor configuration is depicted in FIG. 8A, in which a first membrane 755 (EZL1) containing at least one enzyme (enzyme 1) of at least two enzyme domain configurations is in proximity to at least one surface of the WE. One or more analyte - substrate enzyme pairs having enzyme 1 convert at least one detectable signal into the WE surface by direct or mediated electron transfer that directly or indirectly corresponds to the analyte concentration. A second membrane 756 (EZL2) having at least one second enzyme (enzyme 2) is located adjacent to 755 ELZ1 and is generally distal from the WE relative to EZL1. One or more resistance domains (RL) 752 can be provided adjacent to EZL2 756 and / or between EZL1 755 and EZL2 756. Different enzymes catalyze the conversion of the same analyte, but at least one enzyme in EZL2 756 provides hydrogen peroxide and at least one other enzyme in EZL1 755 does not provide hydrogen peroxide. Thus, each measurable species (e.g., hydrogen peroxide and other measurable species that are not hydrogen peroxide) generates a signal associated with its concentration.

[0317] For example, in the configuration shown in FIG. 8A, a first analyte diffuses into EZL2 756 through RL 752, resulting in peroxide through interaction with enzyme 2. The peroxide diffuses through at least EZL1 755 to the WE, converting a signal that directly or indirectly corresponds to the first analyte concentration. A second analyte different from the first analyte diffuses through RL 752 and EZL2 756, interacts with enzyme 1, which results in electron transfer to the WE, converting a signal that directly or indirectly corresponds to the second analyte concentration.

[0318] As shown in FIG. 8B, the above configuration is adapted to a conductive wire electrode construct in which at least two different enzyme-containing layers are constructed on the same WE having 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.

[0319] In the above configuration, at least two different enzymes are used and can catalyze the conversion of different analytes. At least one enzyme in EZL2 756 provides hydrogen peroxide, and at least the other enzyme in EZL1 755 does not provide hydrogen peroxide, and for example, provides electron transfer to the WE surface directly or indirectly corresponding to the concentration of the analyte.

[0320] In one embodiment, the inner layer of at least two enzyme domains EZL1, EZL2 755, 756 comprises at least one immobilized enzyme combined with at least one mediator that can facilitate lower bias voltage operation of the WE than in the absence of a mediator. In one embodiment, a potential P1 is used for such direct electron conversion. In one embodiment, at least a portion of the inner layer EZL1 755 is more proximal to the WE surface and may have one or more intervening electrode domains and / or overlay interference and / or biointerface and / or drug release membrane, provided that at least one mediator can facilitate low bias voltage operation with the WE surface. In another embodiment, at least a portion of the inner layer EZL1 755 is directly adjacent to the WE.

[0321] The second layer (outer layer EZL2 756) of at least the dual enzyme domain of FIG. 8B contains at least one enzyme that results in one or more catalytic reactions that ultimately produce an amount of hydrogen peroxide capable of electrochemically converting a signal corresponding to the concentration of the analyte. In one embodiment, the generated hydrogen peroxide reaches the WE surface and diffuses through layer EZL2 756 and through the inner layer EZL1 755 to undergo redox at the potential of P2 where P2≠P1. In this way, electron transfer and electrolysis (redox) 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-difference 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 embodiment, a phase shift (e.g., time lag) can result from detecting two signals from two different working electrodes, each signal being generated by a different EZL (EZL1 755, EZL2 756) associated with each electrode. The two (or more) signals can be decomposed into components to detect the individual signals and the signal artifacts generated by each of EZL1 755 and EZL2 756 in response to the detection of two analytes. In some embodiments, each EZL detects a different analyte. In other embodiments, both EZLs detect the same analyte.

[0322] In another alternative exemplary configuration, as shown in FIGS. 8C-8D, for a continuous multi-analyte sensor device that uses a single WE having two or more active surfaces, the multi-enzyme domain configuration described above is provided. In one embodiment, the multi-enzyme domain configuration discussed herein is formed on a planar substrate. In another embodiment, the single WE is coaxial, for example, configured as a wire and having two or more active surfaces located around the longitudinal axis of the wire. An additional wire can be used, for example, as a reference electrode and / or a counter electrode. In another embodiment, the single WE is a conductive trace on a substrate and two or more active surfaces are located around the longitudinal axis of the trace. At least a portion of the two or more active surfaces is discontinuous, providing at least two physically separated WE surfaces on the same WE wire or trace. (For example, 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 ketone.

[0323] Accordingly, FIGS. 8C - 8D depict an exemplary configuration of a continuous multi - analyte sensor configuration in which EZL1 755, EZL2 756, and RL752 (resistance domain) as described above are disposed on a single coaxial wire including spatially separated electrode surfaces WE1, WE2, for example, by sequential dip - coating techniques. One or more parameters such as enzyme domains, resistance domains, etc. can be controlled independently 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 FIGS. 8C - 8D, WE1 represents a first working electrode surface configured to operate at, for example, P1 and is electrically insulated from a second working electrode surface WE2 configured to operate at P2, and RE represents a reference electrode RE that is electrically insulated from both WE1 and WE2. In the configuration of FIG. 8C, one resistance domain covering the reference electrode and WE1, WE2 is provided. In the configuration of FIG. 8D, an additional resistance domain extending essentially only over WE2 is provided. Additional electrodes such as counter electrodes can be used. Such configurations (whether single - wire or dual - wire configurations) can also be used to measure the same analyte using two different techniques. Using different signal generation sequences as well as different RLs, data collected from two different measurement modes provides increased fidelity, improved performance, and device lifetime. Non - limiting examples are glucose oxidase (H2O2 production) and glucose dehydrogenase (electrically coupled) configurations. Measurement of glucose from two different electrodes at two different potentials provides more data points and accuracy. Such an approach may not be required for glucose sensing but can be applied across the biomarker sensing spectrum of other analytes, either alone or in combination with glucose sensing such as ketone sensing, ketone / lactate sensing, and ketone / glucose sensing.

[0324] In an alternative configuration to that depicted in FIGS. 8C - 8D, two or more wire electrodes are presented that are on the same straight line, can be wound, or otherwise juxtaposed, and WE1 is separated from WE2, for example, from other electrodes of elongated shape. The insulating layer electrically insulates WE1 from WE2. In this configuration, independent electrode potentials can be applied to the corresponding electrode surfaces, and the independent electrode potentials can be provided to WE1, WE2 simultaneously, sequentially, or randomly. In one embodiment, the electrode potentials applied to the corresponding electrode surfaces WES1, WES2 are different. One or more additional electrodes, such as reference electrodes and / or counter electrodes, can be present. In one embodiment, WES2 is longitudinally distal to WES1 in an elongated arrangement. For example, using the dip - coating method, WES1 and WES2 are coated with the enzyme domain EZL1, while WES2 is coated with a different enzyme domain EZL2. Based on the dip parameters, or different thicknesses of the enzyme domains, multi - layer enzyme domains can...

Claims

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

2. The continuous analyte sensor comprises: A substrate; A working electrode arrayed on the substrate; and A reference electrode arrayed on the substrate, wherein the analyte measurement generated by the continuous analyte sensor corresponds to an electromotive force at least partially based on a potential difference generated between the working electrode and the reference electrode. The monitoring system according to claim 1.

3. The continuous analyte sensor includes a continuous glucose sensor, and The analyte measurement includes a glucose measurement. The monitoring system according to claim 1.

4. A memory including executable instructions; and One or more processors in data communication with the memory and configured to execute the executable instructions, wherein the instructions cause: Receiving the glucose measurement from the sensor electronics module, the glucose measurement including: A first set of glucose measurements associated with one or more pre-treatment periods; A second set of glucose measurements associated with one or more treatment periods; or A third set of glucose measurements associated with one or more post-treatment periods; Processing the glucose measurement to: Determine one or more first glucose metrics associated with a change in the first set of glucose measurements; Determine one or more second glucose metrics associated with a change in the second set of glucose measurements; or Determine one or more third glucose metrics associated with a change in the third set of glucose measurements; Creating one or more physiological profiles, the physiological profile including: A pre-treatment physiological profile corresponding to the one or more first glucose metrics; A treatment physiological profile corresponding to the one or more second glucose metrics; or A post-treatment physiological profile corresponding to the one or more third glucose metrics; Determining that the patient is in a period corresponding to a pre-treatment period, a treatment period, or a post-treatment period. To determine the likelihood of a health - harmful event, where the likelihood is the determined period, at least one of the pre - treatment physiological profile, the during - treatment physiological profile, or the post - treatment physiological profile, or a plurality of glucose measurements associated with at least one of the determined period or a period prior to the determined period, and based on the likelihood, to generate at least one of one or more recommended or optimized treatment parameters, the monitoring system according to claim 3, which causes this to be done.

5. Further comprising one or more non - analyte sensors, wherein the processor is further configured to receive non - analyte sensor data generated for the patient using the one or more non - analyte sensors, the pre - treatment physiological profile, the during - treatment physiological profile, and the post - treatment physiological profile are created further based on the non - analyte sensor data, the determined likelihood is further based on a set of non - analyte sensor data associated with the determined period or a period prior to the determined period, the monitoring system according to claim 4.

6. The one or more non - analyte sensors include at least one of an insulin pump, a tactile sensor, an ECG sensor, a heart rate monitor, a blood pressure sensor, a respiratory sensor, a peritoneal dialysis device, or a hemodialysis device, the monitoring system according to claim 5.

7. The glucose metric includes a glucose rate of change, the monitoring system according to claim 4.

8. The physiological profile corresponds to a pattern of the corresponding glucose metric, the monitoring system according to claim 4.

9. The health - harmful event includes at least one of hypokalemia, hyperkalemia, hypoglycemia, hyperglycemia, a cardiac event, or death, the monitoring system according to claim 4.

10. The optimized treatment parameters include at least one of the type of treatment, the dosage of treatment, the activity rate, the activity duration, the activity timing, or treatment parameters optimized for dialysis, the monitoring system according to claim 4.

11. The monitoring system according to claim 10, wherein the treatment parameters optimized for the dialysis include at least one of the type of dialysate composition, the type of dialysate concentration, the type of dialysis membrane, the flow rate, the timing of treatment, the frequency of treatment, or the length of treatment.

12. The monitoring system according to claim 4, wherein the processor is further configured to control the operation of the medical device using one or more of the optimized treatment parameters.

13. The monitoring system according to claim 4, wherein the one or more recommended or optimized treatment parameters are generated using a model trained based on population data including records of historical patients indicating various treatment parameters corresponding to various treatments.

14. The continuous analyte sensor includes a continuous potassium sensor, The monitoring system according to claim 1, wherein the analyte measurement values include potassium measurement values.

15. A memory including executable instructions, One or more processors configured to communicate with the memory and execute the executable instructions, and by the instructions, Receiving the potassium measurement value from the sensor electronics module, wherein the potassium measurement value A first set of potassium measurement values associated with one or more pre-treatment periods, A second set of potassium measurement values associated with one or more treatment periods, or A third set of potassium measurement values associated with one or more post-treatment periods, including, Processing the potassium measurement value to Determining one or more first potassium metrics associated with a change in the first set of potassium measurement values, Determining one or more second potassium metrics associated with a change in the second set of potassium measurement values, or Determining one or more third potassium metrics associated with a change in the third set of potassium measurement values, Creating one or more physiological profiles, wherein the physiological profiles A pre-treatment physiological profile corresponding to the one or more first potassium metrics, A treatment physiological profile corresponding to the one or more second potassium metrics, or A post-treatment physiological profile corresponding to the one or more third potassium metrics, including, Determining that the patient is in a period corresponding to a pre-treatment period, a treatment period, or a post-treatment period. To determine the likelihood of a health - harmful event, where the likelihood is based on the determined period, at least one of the pre - treatment physiological profile, the during - treatment physiological profile, or the post - treatment physiological profile, or a plurality of potassium measurements associated with at least one of the determined period or a period prior to the determined period, and based on the likelihood, to generate at least one of one or more recommended or optimized treatment parameters, the monitoring system according to claim 14, which causes the above to be performed. **Claim 16** Further comprising one or more non - analyte sensors, wherein the processor is further configured to receive non - analyte sensor data generated for the patient using the one or more non - analyte sensors, the pre - treatment physiological profile, the during - treatment physiological profile, and the post - treatment physiological profile are created further based on the non - analyte sensor data, the determined likelihood is further based on a set of non - analyte sensor data associated with the determined period or a period prior to the determined period, the monitoring system according to claim 15. **Claim 17** The one or more non - analyte sensors include at least one of an insulin pump, a tactile sensor, an ECG sensor, a heart rate monitor, a blood pressure sensor, a respiratory sensor, a peritoneal dialysis device, or a hemodialysis device, the monitoring system according to claim 16. **Claim 18** The potassium metric includes a potassium change rate, the monitoring system according to claim 15. **Claim 19** The optimized treatment parameters include at least one of the type of treatment, the dosage of treatment, the activity rate, the activity duration, the activity timing, or treatment parameters optimized for dialysis, the monitoring system according to claim 15. **Claim 20** The treatment parameters optimized for dialysis include at least one of the type of dialysis fluid composition, the type of dialysis fluid concentration, the type of dialysis membrane, the flow rate, the timing of treatment, the frequency of treatment, or the length of treatment, the monitoring system according to claim 19.

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