Systems and methods for computing glycemic control change

Abstract

The present disclosure describes an analyte monitoring system that tracks a host's glycemic control levels over time to determine disease progression. The analyte monitoring system may then administer a treatment to address the disease progression.

Description

SYSTEMS AND METHODS FOR COMPUTING GLYCEMIC CONTROL CHANGE CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 725,238, filed November 26, 2024, which is incorporated by reference herein in its entirety, and is hereby expressly made a part of this specification.INTRODUCTION

[0002] Diabetes mellitus is a metabolic condition relating to the production or use of insulin by the body. Insulin is a hormone that allows the body to use glucose for energy, or store glucose as fat.

[0003] When a person eats a meal that contains carbohydrates, the digestive system absorbs nutrients, ultimately depositing glucose in the person's blood. Blood glucose can be used for energy or stored as fat. The body normally maintains blood glucose levels in a range that provides sufficient energy to support bodily functions and avoids problems that can arise when glucose levels are too high, or too low. Regulation of blood glucose levels depends on the production and use of insulin, which regulates the movement of blood glucose into cells.

[0004] When the body does not produce enough insulin, or when the body is unable to effectively use insulin that is present, blood sugar levels can elevate beyond normal ranges. The state of having a higher than normal blood sugar level is called “hyperglycemia.” Chronic hyperglycemia can lead to a number of health problems, such as cardiovascular disease, cataract and other eye problems, nerve damage (neuropathy), skin ulcers, and kidney damage. Hyperglycemia can also lead to acute problems, such as diabetic ketoacidosis — a state in which the body becomes excessively acidic due to the production of excess ketones, or body acids. The state of having lower than normal blood glucose levels is called “hypoglycemia.” Severe hypoglycemia can lead to damage of the heart muscle, neurocognitive dysfunction, and in certain cases, acute crises that can result in seizures or even death.

[0005] A patient living with diabetes can receive insulin to manage blood glucose levels. Insulin can be received, for example, through a manual injection with a needle. Wearable insulin pumps are also available. Diet and exercise also affect blood glucose levels.

[0006] Diabetes conditions are sometimes referred to as “Type 1” and “Type 2.” A Type 1 diabetes patient is typically able to use insulin when it is present, but the body is unable toproduce sufficient amounts of insulin, because of a problem with the insulin-producing beta cells of the pancreas. A Type 2 diabetes patient may produce some insulin, but the patient has become “insulin resistant” due to a reduced sensitivity to insulin. The result is that even though insulin is present in the body, the insulin is not sufficiently used by the patient's body to effectively regulate blood sugar levels.

[0007] Patients with diabetes can benefit from real-time diabetes management guidance, as determined based on a physiological state of the patient, in order to stay within a target glucose range and avoid physical complications. In certain cases, the physiological state of the patient is determined using monitoring systems that measure glucose levels, which inform the identification and / or prediction of adverse glycemic events, such as hyperglycemia and hypoglycemia, and the type of guidance provided to the patient.

[0008] For example, such monitoring systems may utilize a continuous glucose monitor (CGM) to measure a patient’s glucose levels over time. The measured glucose levels may then be processed by the monitoring system to identify and / or predict adverse glycemic events, and / or to provide guidance to the patient for treatment and or actions to abate or prevent the occurrence of such adverse glycemic events. For example, trends, statistics, or other metrics may be derived from the glucose levels and used to identify and / or predict adverse glycemic events. Or, in certain cases, the glucose levels themselves may be used to identify and / or predict adverse glycemic events.

[0009] However, even with the systems described above, the management of diabetes presents many challenges for patients, clinicians, and caregivers, as a confluence of various factors can impact a patient's glucose levels, thus affecting the accuracy of glycemic event prediction and the guidance provided by diagnostics systems.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To understand this disclosure in detail, reference is made to the following description and accompanying drawings. It is to be noted, however, that the appended drawings illustrate only certain typical aspects of this disclosure and are therefore not to be considered limiting of its scope, for the description may admit to other equally effective aspects.

[0011] FIG. 1 illustrates aspects of an example continuous analyte monitoring system used in connection with implementing embodiments of the present disclosure.

[0012] FIG. 2 is a diagram conceptually illustrating example components of the continuous analyte monitoring system of FIG. 1, including example continuous analyte sensor(s) with sensor electronics, according to certain embodiments of the present disclosure.

[0013] FIG. 3 illustrates example inputs and example metrics that are calculated based on the inputs for use by the continuous analyte monitoring system of FIG. 1, according to certain embodiments of the present disclosure.

[0014] FIG. 4 illustrates an example operation for determining and administering a treatment performed by the continuous analyte monitoring system of FIG. 1.

[0015] FIG. 5 illustrates an example operation for determining glycemic control levels performed by the continuous analyte monitoring system of FIG. 1.

[0016] FIG. 6 illustrates an example operation for determining glycemic control levels performed by the continuous analyte monitoring system of FIG. 1.

[0017] FIG. 7 illustrates an example operation for determining disease progression performed by the continuous analyte monitoring system of FIG. 1.

[0018] FIG. 8 illustrates an example operation for determining and administering a treatment performed by the continuous analyte monitoring system of FIG. 1.

[0019] FIG. 9 is a flowchart of an example method for determining and administering a treatment performed by the continuous analyte monitoring system of FIG. 1.

[0020] FIG. 10 is a block diagram depicting a computer system, according to certain embodiments of the present disclosure.

[0021] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements disclosed in one aspect may be beneficially utilized on other aspects without specific recitation.DETAILED DESCRIPTION

[0022] In a continuous analyte monitoring system, a transcutaneous continuous analyte sensor that is inserted into the interstitial fluid is used to monitor a user’s analyte levels (e.g., glucose levels), thereby, providing analyte concentration measurements reflective of the physiological state of the user. An analyte may be understood as any substance of interest that is to be measured or is being measured. Examples of such analytes include glucose, ketones, lactate, insulin, electrolytes, creatinine, as well as a number of other biomarkers including proteins, metabolites, and nucleic acids. The sensor electrodes of the transcutaneous continuous analyte sensor may interact with the desired analyte (e.g., through aptamers (singlestranded DNA or RNA molecules that bind to a specific analyte)). An analyte of particular interest may be referred to as a target analyte. For example, in a transcutaneous continuous glucose sensor, the target analyte may be glucose.

[0023] The concentration of the target analyte in the user may cause the continuous analyte sensor to generate an electric signal (e.g., an electric current or voltage). The continuous analyte sensor converts the electric signal into a signal that includes a time series of data points, with each data point indicating a concentration of the target analyte in the user at a particular time. The time series of data points may be presented on a display device of the continuous analyte monitoring system so the user can visualize the target analyte concentration in the user over time. When an analyte concentration (e.g., glucose concentration) changes rapidly or beyond normal levels (e.g., spikes), the continuous analyte monitoring system may determine that a metabolic event is occurring.

[0024] Existing analyte monitoring systems, however, are limited in providing additional information that would help assess and determine disease progression and potential treatments. As a result, existing systems may provide analyte measurements, but this data may be subject to misinterpretation, which may result in incorrect treatment. Additionally, there is a lack of available technical solutions that address the complexity faced by healthcare providers to effectively and efficiently distill and assess all relevant and available clinical data to determine disease progression and potential treatment considerations given the volume of data and the interconnected and interdependent nature of the data. The lack of technical solutions results in clinical inertia within chronic disease management.

[0025] The present disclosure describes an analyte monitoring system (e.g., including an analyte sensor system, a display device such as a phone, and a backend system) that tracks multiple types of data to determine disease progression and treatment options. For example, the analyte monitoring system may track fasting glucose levels and post-meal glucose levels (e.g., within two to three hours following logged meals) over time. This information, when analyzed over time, may reveal changes in the user’s glycemic control (e.g., whether glycemic control is improving, staying the same, or getting worse). The analyte monitoring system may use the change in the user’s glycemic control along with a user’s disease type (e.g., diabetes type) or pregnancy status / due date to determine disease progression (e.g., whether the user is at risk of diabetes, whether a user at risk for diabetes develops pre-diabetes, or whether a user with pre-diabetes progresses to having diabetes or gestational diabetes).

[0026] The analyte monitoring system may also use other information to determine and administer treatment options for the determined glycemic control and / or disease progression. For example, the analyte monitoring system may be provided a user’ s meal information, sleep information, activity and stress levels, medications taken, glucose management indicator (GMI), weight status, blood pressure, non-glycemic sensor analyte levels, body mass index (BMI), other clinical markers, social determinants of health, and co-morbidities that could influence treatment decisions (e.g., chronic kidney disease, obesity, cardiovascular disease, metabolic syndrome, retinopathy, neuropathy, foot ulcers, etc.). The analyte monitoring system may consider this information to determine pharmacological interventions (e.g., take new medications, change medications, take medications at different times or with different doses, etc.), behavioral modifications (e.g., exercise routines, diet, sleep, etc.), and / or recommended screenings (e.g., retinopathy, nephropathy, neuropathy, foot ulceration, peripheral arterial disease, etc.). The information may also be used to determine other information (e.g., health insurance rates and reimbursements).

[0027] In certain embodiments, the analyte monitoring system provides several technical advantages. For example, the analyte monitoring system tracks analyte levels over time to determine disease progression and to administer treatments. As a result, the analyte monitoring system reduces clinical inertia and improves the health and safety of patients.

[0028] As used herein, the term “continuous” analyte monitoring refers to monitoring one or more analytes in a fully continuous or semi-continuous, periodic manner, which results in adata stream of analyte values over time. A data stream of analyte values over time is what allows for meaningful data and insight to be derived using the algorithms described herein. In other words, single point-in-time measurements collected as a result of a patient visiting their health care professional every few months results in sporadic data points (e.g., that are, at best, months apart in timing) that cannot form the basis of meaningful data or insight to be derived.

[0029] Further, the data stream of analyte values collected over time, with the continuous analyte monitoring system presented herein, include real-time analyte values, which allows for deriving meaningful data and insight in real-time using the systems and algorithms described herein. Real time analyte values herein refer to analyte values that become available and actionable within seconds or minutes of being produced as a result of at least one sensor electronics module of the continuous analyte monitoring system (1) converting sensor analog electrical signals (e.g., electric current or voltage) generated by the continuous analyte sensor(s) into sensor count values, (2) calibrating the count values to generate analyte concentration values using calibration techniques described herein to account for the sensitivity of the continuous analyte sensor(s), and (3) transmitting measured analyte concentration data, including analyte concentration values, to a display device via wireless connection.

[0030] For example, the continuous analyte monitoring system may sample the analog electrical signals at a particular sampling period (or rate), such as every 1 second (1 Hz), 5 seconds, 10 seconds, 30 seconds, 1 minute, 3 minutes, 5 minutes, etc., and transmit the measured analyte concentration data to a display device at a particular transmission period (or rate), which may be the same as (or longer than) the sampling period, such as every 1 minute (0.016 Hz), 5 minutes, 10 minutes, etc.

[0031] Because of the real-time nature of the data generated by the continuous analyte monitoring system, it is humanly impossible to continuously process a real-time data stream of analyte values over time to derive meaningful data and insight using the algorithms and systems described herein. In other words, deriving meaningful data and insight from a stream of realtime data that is continuously generated, processed, calibrated, and analyzed, using the algorithms and systems described herein, is not a task that can be mentally performed. For example, executing the algorithms described herein in real-time and on a continuous basis, which would involve using a stream of real-time data that is continuously generated by acontinuous analyte monitoring system and / or significantly large amount of data is not a task that can be mentally performed, especially in real-time.

[0032] FIG. 1 illustrates an example of a continuous analyte monitoring system 100, in accordance with certain embodiments of the disclosure. As seen in FIG. 1, the continuous analyte monitoring system 100 includes an analyte sensor system 104, a display device 107 that executes an application 106, a host database 110, an historical records database 112, a training server system 140, and a management engine 114, each of which is described in more detail below.

[0033] The analyte sensor system 104 generates time-series data, such as analyte measurements (e.g., sensor data), for the user 102 (e.g., on a continuous basis) and transmits the analyte measurements to the display device 107 for use by the application 106. The analyte sensor system 104 may transmit the analyte measurements to the display device 107 through a wireless connection (e.g., Bluetooth connection). The display device 107 may be a smart phone or any other type of computing device capable of executing the application 106, such as a laptop computer, a smartwatch, and / or a tablet.

[0034] In certain examples, the analyte sensor system 104 is assumed to be a glucose monitoring system, but the analyte sensor system 104 may operate to monitor one or more additional or alternative analytes. As discussed, the term “analyte” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a substance or chemical constituent in the body or a biological sample (e.g., bodily fluids, including, blood, serum, plasma, interstitial fluid, cerebral spinal fluid, lymph fluid, ocular fluid, saliva, oral fluid, urine, excretions, or exudates).

[0035] Analytes can include naturally occurring substances, artificial substances, metabolites, and / or reaction products. In some embodiments, the analyte measured and used by the devices and methods described herein may include albumin, alkaline phosphatase, alanine transaminase, aspartate aminotransferase, bilirubin, blood urea nitrogen, calcium, CO2, chloride, creatinine, glucose, gamma-glutamyl transpeptidase, hematocrit, lactate, lactate dehydrogenase, magnesium, oxygen, pH, phosphorus, potassium, ketones, sodium, total protein, uric acid, metabolic markers, and / or drugs.

[0036] Other analytes are contemplated as well, including but not limited to acetaminophen, dopamine, ephedrine, terbutaline, ascorbate, uric acid, oxygen, d-amino acid oxidase, plasma amine oxidase, xanthine oxidase, NADPH oxidase, alcohol oxidase, alcohol dehydrogenase, pyruvate dehydrogenase, diols, Ros, NO, bilirubin, cholesterol, triglycerides, gentisic acid, ibuprophen, L-Dopa, methyl dopa, salicylates, tetracycline, tolazamide, tolbutamide, acarboxyprothrombin; acylcarnitine; adenine phosphoribosyl transferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; camosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-P hydroxycholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporin A; d- penicillamine; de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcohol dehydrogenase, alpha 1 -antitrypsin, cystic fibrosis, Duchenne / Becker muscular dystrophy, glucose-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sexual differentiation, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diptheria / tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids / acylglycines; free P-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine (FT3); fumarylacetoacetase; galactose / gal-1 -phosphate; galactose- 1 -phosphate uridyltransferase; gentamicin; glucose-6-phosphate dehydrogenase; glutathione; glutathione perioxidase; glycocholic acid; glycosylated hemoglobin; halofantrine; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase; immunoreactive trypsin; lactate; lead; lipoproteins ((a), B / A-l, P); lysozyme; mefloquine; netilmicin; phenobarbitone; phenyloin; phytanic / pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3); selenium; serum pancreatic lipase; sissomicin; somatomedin C; specific antibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody, arbovirus, Aujeszky's disease virus, dengue virus, Dracunculus medinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus, Giardia duodenalisa, Helicobacter pylori,hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani, leptospira, measles / mumps / rubella, Mycobacterium leprae, Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenza virus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, rickettsia (scrub typhus), Schistosoma mansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosoma cruzi / rangeli, vesicular stomatis 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; white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat, vitamins, and hormones naturally occurring in blood or interstitial fluids can also constitute analytes in certain embodiments.

[0037] The analyte can be naturally present in the biological fluid, for example, a metabolic product, a hormone, an antigen, an antibody, and the like. Alternatively, the analyte can be introduced into the body, for example, a contrast agent for imaging, a radioisotope, a chemical agent, a fluorocarbon-based synthetic blood, or a drug or pharmaceutical composition, including but not limited to insulin; ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin, Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine); depressants (barbituates, methaqualone, tranquilizers such as Valium, Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogs of fentanyl, meperidine, amphetamines, methamphetamines, and phencyclidine, for example, Ecstasy); anabolic steroids; and nicotine. The metabolic products of drugs and pharmaceutical compositions are also contemplated analytes. Analytes such as neurochemicals and other chemicals generated within the body can also be analyzed, such as ascorbic acid, uric acid, dopamine, noradrenaline, 3 -methoxy tyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5 -hydroxy tryptamine (5HT), histamine, Advanced Glycation End Products (AGEs) and 5-hydroxyindoleacetic acid (FHIAA).

[0038] The application 106 may be a mobile health application that receives and analyzes time-series data, including analyte measurements, from the analyte sensor system 104 and / or other devices. The application 106 may transmit analyte measurements received from the analyte sensor system 104 to the host database 110 (and / or the historical records database 112), and the host database 110 (and / or the historical records database 112) may store the analyte measurements in a host profile 118 of the user 102 for processing and analysis, for example, by the training server system 140 or the management engine 114, based on contextual data supplied by the user 102. In some embodiments, the application 106 may store the analyte measurements in the host profile 118 of the user 102 locally for processing and analysis, for example, by the training server system 140 or the management engine 114, based on contextual data supplied by the user 102.

[0039] The application 106 may provide and present various interfaces for receiving, from the user 102, contextual data of the type discussed previously. In an example, the application 106 provides a user interface that allows the user 102 to log actions that the user 102 performed. In another example, the application 106 provides a user interface that allows the user 102 to link actions to detected metabolic events. The application 106 may also provide a user interface that presents information about detected metabolic events (e.g., the measured analyte concentrations during the events).

[0040] The application 106 may take as input information relating to the user 102 and store the information in a host profile 118 for the user 102 in the host database 110. For example, the application 106 may obtain and record the analyte concentration measurements for the user 102, the metabolic events detected in the user 102, and / or the actions logged by the user 102 in the user profile 118. The application 106, the training server system 140, and / or the management engine 114 may treat the information in the user profile 118 as historical data about the user 102. This historical data may provide insights about the user 102, such as the actions that cause certain metabolic events in the user 102 and the analyte concentrations or changes in the analyte concentrations that indicate that metabolic events are occurring in the user 102.

[0041] The application 106 collects inputs through user input and / or other sources, including the analyte sensor system 104, other applications running on the display device 107, and / or one or more other sensors and devices. These sensors and devices may include one ormore of, but are not limited to, an insulin pump, other types of analyte sensors, sensors or devices provided by the display device 107 (e.g., accelerometer, camera, global positioning system (GPS), heart rate monitor, etc.), other user accessories (e.g., a smartwatch), or any other sensors or devices that provide relevant information about the user 102. In certain embodiments, the host profile 118 also stores application configuration information indicating the current configuration of the application 106, including features and settings.

[0042] The host database 110 may be a storage server that operates in a public or private cloud. The host database 110 may be implemented as any type of data store, such as relational databases, non-relational databases, key-value data stores, file systems including hierarchical file systems, and the like. In some implementations, the host database 110 is distributed. For example, the host database 110 may include persistent storage devices, which are distributed. Furthermore, the host database 110 may be replicated so that the storage devices are geographically dispersed.

[0043] The host database 110 may include other host profiles 118 for other users. Similar to the operations performed with respect to the user 102, the operations performed with respect to these other users may utilize an analyte monitoring system, such as analyte sensor system 104, and also interact with the same application 106, copies of which execute on the respective display devices of the other users. For such users, the host profiles 118 are similarly created and stored in the host database 110.

[0044] Further, in certain embodiments, the host database 110 may store data for other users or people. For example, the host database 110 may include information (e.g., profile(s)) related to one or more users or hosts known to be metabolically unfit. Data stored in the host database 110 may be referred to herein as population data, which could include hundreds or thousands of data points for each one of thousands or millions of people in the population. In other words, data stored in the host database 110 and used in certain embodiments described herein could include gigabytes, terabytes, petabytes, exabytes, etc. of data.

[0045] The host profile 118 may include application data 128 collected about the user 102 from the application 106. For example, the application 106 provides a set of inputs 130, including the analyte measurements received from continuous analyte monitoring system 104. In certain embodiments, the inputs 130 provided by the application 106 include other data in addition to analyte measurements. For example, the application 106 may obtain additionalinputs 130 through manual host input, one or more other non-analyte sensors or devices, other applications executing on the display device 107, etc.

[0046] The host profile 118 also includes demographic information 120, physiological information 122, disease information 124, medication information 126, and / or social determinants of health information 127 for the user 102. In certain embodiments, such information may be provided through user input or obtained from certain data stores (e.g., electronic medical records (EMRs), etc.). The demographic information 120 may include one or more of the age, ethnicity, gender, etc. of the user 102. The physiological information 122 may include one or more of the height, weight, cardiovascular health, body mass index (BMI), pregnancy status, and / or due date of the user 102. The disease information 124 may include information about one or more diseases of the user 102, including relevant information pertaining to cardiovascular disease or congenital heart defects (CHDs), kidney disease, kidney dysfunction, and / or acute kidney injury, liver disease and / or liver dysfunction, diabetes, lung diseases, adrenal gland disorders, and / or other health conditions, syndromes, or diseases. The disease information 124 may also include the length of time since diagnosis, disease progression information, the level of disease control, level of compliance with disease management therapy, other types of diagnoses (e.g., obesity, hormone imbalances, hyperkalemia or hypokalemia, etc.), and the like. The disease information 124 may include hospitalizations and / or surgical history. In some instances, the disease information 124 may include other measures of health (e.g., heart rate, heart rhythm, blood pressure, stress, sleep, etc.) or fitness (e.g., cardiovascular endurance, metabolic state, muscular endurance, and other measures of fitness), and / or the like.

[0047] The medication information 126 may include information about the amount, frequency, and / or type of a medication taken by, or prescribed to, the user 102. The amount, frequency, and type of a medication taken by the user 102 is time-stamped and correlated with the analyte levels of the user 102, thereby, indicating the impact the amount, frequency, and type of the medication had on the analyte levels. The medication information 126 may include information about the consumption of one or more drugs, including renin-angiotensin- aldosterone system inhibitors (RAASi), mineral corticoid receptor antagonists (MRAs), angiotensin-converting enzyme inhibitors (ACE inhibitors), angiotensin-receptor blockers (ARBs), other heart drugs such as amiodarone and clopidogrel, diuretics (which may be usedto treat excessive fluid accumulation caused by, for example, heart failure (HF), liver failure, and / or nephritic syndrome) such as loop diuretics, thiazide and thiazide-like diuretics, and potassium- sparing diuretics, antibiotics such as amoxicillin / clavulanate, clindamycin, erythromycin, nitrofurantoin, rifampin, sulfonamides, tetracyclines, trimethoprim / sulfamethoxazole, vancomycin, and drugs used to treat tuberculosis (isoniazid and pyrazinamide), anticonvulsants such as tarbamazepine, thenobarbital, phenytoin, and valproate, antidepressants such as bupropion, fluoxetine, mirtazapine, paroxetine, sertraline, trazodone, and tricyclic antidepressants such as amitriptyline, antifungal drugs such as ketoconazole and terbinafine, antihypertensive drugs (e.g., drugs used to treat high blood pressure or sometimes kidney or heart disorder) such as captopril, enalapril, irbesartan, lisinopril, losartan, and verapamil, antipsychotic drugs such as phenothiazines (e.g., such as chlorpromazine) and risperidone, hormone regulation drugs such as anabolic steroids, birth control pills (oral contraceptives), and estrogens, pain relievers such as acetaminophen and nonsteroidal anti-inflammatory drugs (NSAIDs), and other drugs such as acarbose (e.g., used to treat diabetes), allopurinol (e.g., used to treat gout), antiretroviral therapy (ART) drugs (e.g., used to treat human immunodeficiency virus (HIV) infection), baclofen (e.g., a muscle relaxant), cyproheptadine (e.g., an antihistamine), azathioprine (e.g., used to prevent rejection of an organ transplant), methotrexate (e.g., used to treat cancer), omeprazole (e.g., used to treat gastroesophageal reflux), PD-1 / PD-L1 inhibitors (e.g., anticancer drugs), statins (e.g., used to treat high cholesterol levels), ademetionine, avatrombopag, dehydroemetine, entecavir, glecaprevir and pibrentasvir, lamivudine, lithium, metadoxine, methionine, sofosbuvir, velpatasvir, and voxilaprevir, telbivudine, tenofovir, trientine, tacrolimus and other calcineurin inhibitors, ursodeoxycholic acid, many types of chemotherapies, including immune checkpoint inhibitors, drugs for treatment of acute hyper- and / or hypokalemia, such as insulin, dextrose, and glucose, and the like.

[0048] The social determinants of health 127 may indicate non-medical factors that affect health outcomes for the user 102. For example, the social determinants of health 127 may include the condition in which the user 102 is born, grows, works, lives, and / or ages. The social determinants of health 127 may indicate factors such as access to healthcare, access to education, economic stability, safe housing, transportation, neighborhoods, pollution, access to food, etc.

[0049] The host profile 118 may be dynamic because at least part of the information that is stored in the host profile 118 may be revised or updated over time and / or new information may be added to the host profile 118 by the management engine 114 and / or the application 106. Accordingly, information in the host profile 118 stored in the host database 110 provides an up-to-date repository of information related to the user 102.

[0050] The host profile 118 stored in the host database 110 may also be stored in the historical records database 112. The historical records database 112 may provide a repository of up-to-date information and historical information for the user 102. Thus, the historical records database 112 essentially provides data related to the user 102, where data is stored using timestamps. The timestamp associated with any piece of information stored in the historical records database 112 may identify, for example, when the piece of information was obtained and / or updated.

[0051] Further, the historical records database 112 may include data collected for one or more users over a period of time, including users who are hosts of the continuous analyte sensor system 104 and / or the application 106, as well as users who are not hosts of the continuous analyte sensor system 104 and / or the application 106. For example, the historical records database 112 may include information (e.g., user profile(s)) related to one or more hosts analyzed by, for example, a healthcare physician (or other known method), and not previously diagnosed with heart failure, kidney disease, and / or other indications, as well as information (e.g., user profile(s)) related to one or more hosts who were analyzed by, for example, a healthcare physician (or other known method) and were previously diagnosed with (varying types and stages of) heart failure, kidney disease, and / or other indications.

[0052] Data stored in the historical records database 112 may be referred to herein as population data, which could include hundreds or thousands of data points for each one of thousands or millions of hosts in the host population. In other words, data stored in the historical records database 112 could include gigabytes, terabytes, petabytes, exabytes, etc. of data.

[0053] Data related to each host stored in the historical records database 112 may provide time series data collected over the lifetime of the host, a period of the lifetime of the host, and / or a disease lifetime of the host. For example, the data may include information about the host prior to being diagnosed with HF, kidney disease, and / or other indications, andinformation associated with the patient during the lifetime of the disease, including information related to each stage of the disease as it progressed and / or regressed in the patient, as well as information related to other diseases, such as hyperkalemia, hypokalemia, diabetes, or similar diseases that are co-morbid in relation thereto. The data may also include physiological information (e.g., height and weight), as well as non-analyte sensor data (e.g., heart rate, respiratory rate, etc.). Such data may indicate physiological states of the host, potassium levels of the host, glucose levels of the host, lactate levels of the host, insulin levels of host, other hormone levels of the host, states / conditions of one or more organs of the host, habits of the host (e.g., activity levels, food consumption, etc.), medication prescribed throughout the lifetime of the disease, as well as progress of outcomes such as weight loss and cardiovascular health over time, etc.

[0054] Although depicted as separate databases for conceptual clarity, in certain embodiments, the host database 110 and the historical records database 112 may operate as a single database. That is, historical and current data related to hosts, as well as historical data related to hosts that were not previously hosts, may be stored in a single database. The single database may be a storage server that operates in a public or private cloud.

[0055] The management engine 114 includes a set of software instructions with one or more software modules, including a data analysis module (DAM) 116. The management engine 114 may execute entirely on one or more computing devices in a private or a public cloud. The application 106 communicates with the management engine 114 over a network (e.g., the Internet). The management engine 114 may execute 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 a public cloud. The management engine 114 may execute entirely on one or more local devices, such as the display device 107. As discussed in more detail herein, the management engine 114 may provide therapy management support recommendations to the user 102 via the application 106. The management engine 114 provides therapy management support recommendations based on information included in the host profile 118.

[0056] The DAM 116 of the management engine 114 may process the set of inputs 130 to determine one or more metrics 132. The metrics 132 may generally indicate the health or state of the user 102, such as one or more of the physiological state, trends associated with the health or state, etc. In certain embodiments, the metrics 132 may then be used by the managementengine 114 as input for providing guidance to the user 102. As shown, the metrics 132 are also stored in host profile 118.

[0057] The management engine 114 may (1) provide real-time and or non-real-time therapy management guidance (e.g., guidance) to the user 102 and or others, including but not limited, to healthcare providers, family members of the host, caregivers of the host, etc., and / or (2) provide real-time instructions to an automated medication delivery device for automatically adjusting medication administration, including dose and timing parameters, for the user 102. Therapy management support may be intended to provide optimal medication administration guidance to treat the indication (e.g., prevent development and / or progression of the indicated disease state) of the administered medication.

[0058] In particular, the management engine 114 may collect information associated with the user 102 in the host profile 118 stored in the host database 110 and to perform analytics thereon to determine a risk or presence of a disease. Based on the determination, the management engine 114 may optimize therapy for a disease state of the user 102. Optimizing therapy, as described above, may include providing optimized therapy management guidance to the user 102 and / or automatically controlling the operations of a medication pump, such as by adjusting a flow rate, dose, etc. of the therapy, as well as the timing of the therapy. The host profile 118 may be accessible to the management engine 114 over one or more networks (not shown) for performing such analytics.

[0059] The management engine 114 may utilize one or more rule-based algorithms or trained machine learning models capable of performing analytics on information that the management engine 114 has collected / received from the host profile 118. In the illustrated embodiment of FIG. 1, the management engine 114 may utilize a trained machine learning model provided by the training server system 140. Although depicted as a separate server for conceptual clarity, in certain embodiments, the training server system 140 and the management engine 114 may operate as a single server or system. 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 or systems. In certain embodiments, the model may be trained on one or many virtual machines (VMs) running, at least partially, on one or many physical services in relational and or non-relational database formats.

[0060] The training server system 140 trains the machine learning model(s) using training data, which may include data (e.g., from host profiles) associated one or more hosts (e.g., hosts or non-hosts) previously diagnosed with, for example, varying stages of disease, as well as hosts not previously diagnosed with disease. The training data may be stored in the historical records database 112 and may be accessible to the training server system 140 over one or more networks (not shown) for training the machine learning model(s).

[0061] The training data refers to a dataset that has been featurized and labeled. For example, the dataset may include a plurality of data records, each including information corresponding to a different host profile stored in the host database 110, where each data record is featurized 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 predictive machine learning models. Data labeling is the process of adding one or more meaningful and informative labels to provide context to the data for learning by the machine learning model. As an illustrative example, each relevant characteristic of a host, which is reflected in a corresponding data record, may be a feature used in training the machine learning model.

[0062] Such features may include demographic information (e.g., age, gender, ethnicity, etc.), analyte information (e.g., analyte levels (e.g., analyte baseline, analyte threshold, analyte clearance rate, and / or analyte rate of change during and after administration of a medication, etc.)), non-analyte sensor information (e.g., heart rate, temperature, etc.), cardiovascular health information (e.g., cardiovascular disease diagnosis (e.g., HF) and staging), co-morbidities (e.g., kidney disease), and / or any other information relevant to optimizing therapy for the hosts. In addition, the data record is labeled with information the corresponding model is being trained to predict. In one example, if a model is being trained to output optimized therapy parameters, then the data records in the training dataset are labeled with one or more of such parameters. Note that, in one example, such a model may be a multi-input single-output (MISO) model that predicts one optimized therapy parameter (e.g., dosage), in which case additional MISO models may be trained, each predicting one of other therapy parameters (e.g., timing, etc.). In another example, such a model may be a multi-input multi-output (MIMO) model that predicts multiple optimized therapy parameters (e.g., dosage and time, etc.).

[0063] The model(s) are then trained by the training server system 140 using the featurized and labeled training data. In particular, the features of each data record may be used as input into the machine learning model(s), and the generated output may be compared to label(s) associated with the corresponding data record. The model(s) may compute a loss based on the difference between the generated output and the provided label(s). This loss is then used to modify the internal parameters or weights of the model. By iteratively processing each data record corresponding to each historical host, the model(s) may be iteratively refined to generate accurate determinations of a host’s risk or presence of potassium imbalance, optimized therapy parameters to treat a disease state of the host and simultaneously stabilize or maintain potassium levels, etc.

[0064] As illustrated in FIG. 1, the training server system 140 deploys these trained model(s) to the management engine 114 for use during runtime. For example, the management engine 114 may obtain the host profile 118 associated with a host and stored in the host database 110, use information in the host profile 118 as input into the trained model(s), and output a determination indicative of the host’s risk or presence of disease, and / or suggested therapy parameters to treat a disease state of the host (e.g., shown as output 144 in FIG. 1). The output 144 generated by the management engine 114 may also indicate improvement in the host’s health over time. The output 144 may be provided to the host (e.g., through the application 106), to a caretaker of the host (e.g., a parent, a relative, a guardian, a teacher, a physical therapist, a fitness trainer, a nurse, etc.), to a physician or healthcare provider of the host, or any other individual that has an interest in the wellbeing of the host for purposes of improving the health of the host, such as, in some cases by effectuating recommended therapy. The output 144 generated by the management engine 114 is stored in the host database 110 and is utilized to train or re-train the trained model(s).

[0065] In certain embodiments, the output 144 generated by the management engine 114 may be stored in the host profile 118. The output 144 stored in the host profile 118 may be continuously updated by the management engine 114. Accordingly, for example, suggested therapy parameters, originally stored as the outputs 144 in the host profile 118 in the host database 110 and then passed to the historical records database 112, may provide an indication of the progression of the physiological status (e.g., disease state) of a host over time, as well as provide an indication as to the effectiveness of different recommendations.

[0066] In certain embodiments, a host’s own historical data may be used by the training server system 140 to train a personalized model for the host that provides therapy management support and insight around the host’s disease. For example, in certain embodiments, a model trained based on population data may be used to provide optimized therapy parameters to the host. However, after collecting personalized information (e.g., analyte sensor information, non-analyte sensor information, etc.) associated with the host during one or more administrations of therapy, the personalized information may be used for further personalizing the model. For example, information obtained during a prior administration of therapy for the host may be used to optimize therapy parameters for future administrations of such therapy.

[0067] In certain embodiments, a model may be trained to provide food, lifestyle, and other types of therapy management support recommendations based on the host’s historical data, including how different types of food and / or activities impacted the host’s health in the past. In certain embodiments, a model may be trained to predict the underlying cause of certain improvements or deteriorations in the host’s analyte levels. For example, application 106 may display a user interface with a graph that shows the host’s analyte levels with trend lines and indicate, e.g., retrospectively, how analyte levels were affected at certain points in time.

[0068] FIG. 2 is a diagram 200 conceptually illustrating example components of the continuous analyte monitoring system 100 of FIG. 1, including example continuous analyte sensor(s) with sensor electronics, according to certain embodiments of the present disclosure. For example, the analyte sensor system 104 may continuously monitor one or more analytes of a host, in accordance with certain aspects of the present disclosure.

[0069] The analyte sensor system 104 in the illustrated embodiment includes sensor electronics module 204 and one or more continuous analyte sensor(s) 202 (individually referred to herein as continuous analyte sensor 202 and collectively referred to herein as continuous analyte sensors 202) associated with the sensor electronics module 204. The sensor electronics module 204 may be in wireless communication (e.g., directly or indirectly) with one or more display devices 210, 220, 230, and 240. In certain embodiments, the sensor electronics module 204 may also be in wireless communication (e.g., directly or indirectly) with one or more medical devices, such as medical devices 208 (individually referred to herein as medical device 208 and collectively referred to herein as medical devices 208), and / or one or more other non-analyte sensors 206 (individually referred to herein as non-analyte sensor 206 and collectively referred to herein as non-analyte sensor 206).

[0070] The continuous analyte sensor 202 may include one or more sensors for detecting and / or measuring analyte(s). The continuous analyte sensor 202 may be a multi-analyte sensor that continuously measures two or more analytes or a single analyte sensor that continuously measures a single analyte as a non-invasive device, a subcutaneous device, a transcutaneous device, a transdermal device, and / or an intravascular device. In certain embodiments, the continuous analyte sensor 202 may continuously measure analyte levels of a host using one or more techniques, such as enzymatic techniques, chemical techniques, physical techniques, electrochemical techniques, spectrophotometric techniques, polarimetric techniques, calorimetric techniques, iontophoretic techniques, radiometric techniques, immunochemical techniques, and the like. The term “continuous,” as used herein, can mean fully continuous, semi-continuous, periodic, etc. In certain aspects, the continuous analyte sensor 202 provides a data stream indicative of the concentration of one or more analytes in the host. The data stream may include raw data signals, which are then converted into a calibrated and / or filtered data stream used to provide estimated analyte value(s) to the host.

[0071] The continuous analyte sensor 202 may be a multi-analyte sensor that continuously measures multiple analytes in a host’s body. For example, the continuous multi-analyte sensor 202 may be a single sensor that measures glucose, lactate, calcium, creatinine, ketones (e.g., 3- beta-hydroxybutyrate, acetoacetate, acetone, etc.), glycerol, and / or free fatty acids in the host’s body.

[0072] In certain embodiments, one or more multi-analyte sensors may be used in combination with one or more single analyte sensors. As an illustrative example, a multianalyte sensor may continuously measure potassium, glucose, and / or lactate, and may, in some cases, be used in combination with an analyte sensor that measures only calcium, ketones, creatinine, or another analyte. Information from each of the multi-analyte sensor(s) and single analyte sensor(s) may be combined to provide therapy management support using methods described herein. Other non-contact and or periodic or semi-continuous, but temporally limited, measurements for physiological information may be integrated into the system such as by including weight scale information or non-contact heart rate monitoring from a sensor pad under the host while in a chair or bed, through an infra-red camera detecting temperature and / orblood flow patterns of the host, and / or through a visual camera with machine vision for height, weight, or other parameter estimation without physical contact.

[0073] The continuous analyte sensor(s) 202 may include a percutaneous wire that has a proximal portion coupled to the sensor electronics module 204 and a distal portion with several electrodes, such as a measurement electrode and a reference electrode. The measurement (or working) electrode may be coated, covered, treated, embedded, etc., with one or more chemical molecules that react with a particular analyte, and the reference electrode may provide a reference electrical voltage. The measurement electrode may generate an analog electrical signal, which is conveyed along a conductor that extends from the measurement electrode to the proximal portion of the percutaneous wire that is coupled to the sensor electronics module 204. After the continuous analyte sensor system 104 has been applied to the epidermis of the host, the continuous analyte sensor(s) 202 penetrates the epidermis, and the distal portion extends into the dermis and / or subcutaneous tissue under epidermis. Other configurations of the continuous analyte sensor(s) 202 may also be used, such as a multi-analyte sensor that includes multiple measurement electrodes, each generating an analog electrical signal that represents the concentration levels of a particular analyte.

[0074] Generally, a single-analyte sensor generates an analog electrical signal that is proportional to the concentration level of a particular analyte. Similarly, each multi-analyte sensor generates multiple analog electrical signals, and each analog electrical signal is proportional to the concentration level of a particular analyte. As an illustrative example, the continuous analyte sensor 202 may include a single-analyte sensor that measures glucose concentration levels, and another single- analyte sensor that measures lactate concentration levels of the patient. As another illustrative example, the continuous analyte sensor(s) 202 may include a single-analyte sensor configured to measure glucose concentration levels, and one or more multi-analyte sensors configured to measure lactate concentration levels, ketone concentration levels, creatinine concentration levels, etc. As yet another illustrative example, the continuous analyte sensor(s) 202 may include a multi-analyte sensor that measures glucose concentration levels, lactate concentration levels, calcium concentration levels, ketone concentration levels, creatinine concentration levels, etc.

[0075] Accordingly, the continuous analyte sensor(s) 202 generates at least one analog electrical signal that is proportional to the concentration level of a particular analyte, and thesensor electronics module 204 converts the analog electrical signal into analyte sensor count values, calibrates the analyte sensor count values based on the sensitivity profile of the continuous analyte sensor(s) 202 to generate measured analyte concentration levels, and transmits the measured analyte concentration level data, including the measured analyte concentration levels, to a display device, such as display devices 210, 220, and / or 230, via a wireless connection. For example, the sensor electronics module 204 may sample the analog electrical signal at a particular sampling period (or rate), such as every 1 second (1 Hz), 5 seconds, 10 seconds, 30 seconds, 1 minute, 3 minutes, 5 minutes, 60 minutes, 4 hours, 8 hours, 12 hours, 24 hours, etc., and transmit the measured analyte concentration data to the display device at a particular transmission period (or rate), which may be the same as (or longer than) the sampling period, such as every 1 minute (0.016 Hz), 5 minutes, 10 minutes, 30 minutes, 60 minutes, 4 hours, 8 hours, 12 hours, 24 hours, at the conclusion of the wear period, etc. Depending on the sampling and transmission periods, the measured analyte concentration data transmitted to the display device may include at least one measured analyte concentration level having an associated time tag, sequence number, etc.

[0076] The continuous analyte sensor(s) 202 may include a thermocouple within, or alongside, the percutaneous wire to provide an analog temperature signal to the sensor electronics module 204, which may be used to correct the analog electrical signal or the measured analyte data for temperature. In other embodiments, the thermocouple may be incorporated into the sensor electronics module 204 above the adhesive pad, or, alternatively, the thermocouple may contact the epidermis of the host through openings in the adhesive pad.

[0077] The continuous analyte sensor(s) 202 may be or include an aptamer sensor, such as an electrochemical aptamer sensor, that continuously measures one or more analytes in a host’s body. Aptamers are peptides or oligonucleotides with high sensitivity and selectivity for the detection of various types of analytes ranging from nucleotides, peptides, proteins, and small molecules to cells. If multiple aptamers are combined into a single continuous analyte sensor 202, the continuous analyte sensor 202 can measure large numbers of different analytes in the host’s body.

[0078] One or more aptamer sensors can be used to directly detect and / or measure an amount of medication(s) administered to a host (e.g., a medication that has entered the body of the host), or can be used to detect and / or measure an amount of protein, enzyme, or othermetabolite of the medication(s) to indirectly detect and / or measure an amount of the medication(s). Accordingly, in such embodiments, the analyte monitoring system 100 may utilize the one or more aptamer sensors to monitor a host’s medication compliance. For example, HF patients are often prescribed multiple medications for treating their disease state, and each medication can have a different administration schedule. One or more aptamer sensors may therefore enable the analyte monitoring system 100 to monitor one or more of the prescribed medications and determine if, and when, each of the one or more medications was taken, and the doses thereof. Information gathered about the medication(s) and the host’s medical compliance can, in turn, inform therapy management guidance (e.g., a suggested timing and / or dose of medication(s) and / or a recommendation for administration of an additional medication) provided by the analyte monitoring system 100.

[0079] The analyte monitoring system 100 may utilize one or more aptamer sensors to assist in titration of one or more medications taken by a host. For example, the one or more aptamer sensors can be used to continuously monitor the effects of the medications on the host (e.g., via measuring one or more levels of the medication itself or a metabolite of the medication). Based on the monitored effects, the analyte monitoring system 100 can provide real-time recommendations to the host and / or a healthcare provider to up-titrate or down-titrate one or more medications to reach a target, or optimal (most effective), medication dose for the host. The recommendations may be based on a measured absolute concentration of a medication detected in a host. In certain embodiments, the measured concentration of the medication can be compared to one or more threshold concentrations to identify a risk or presence of potential adverse events associated with threshold concentrations based on historical population data. If the measured concentration of the medication is determined to be above or below a threshold concentration of the one or more threshold concentrations, the host can be directed to up-titrate or down-titrate their medication dose as appropriate to avoid the potential adverse effects. In certain embodiments, one or more aptamer sensors can monitor a relative change in medication concentration relative to a target metric. The target metric may be based on a desired change in medication concentration when the measured concentration of the medication is associated, or mapped, with one or more negative symptoms (e.g., shortness of breath, potassium imbalance, etc.).

[0080] The analyte monitoring system 100 may utilize one or more aptamer sensors, in combination with other analyte and / or non-analyte sensors as described above, to prevent hospital readmissions of a host. For example, upon discharge from a hospital, the host may be provided a sensor, such as an aptamer sensor, to monitor medication adherence of the host and the efficacy of the prescribed dosage. The analyte monitoring system 100 or a healthcare provider can then monitor the host’s medication adherence, as well as other analyte and / or non- analyte data, to then identify and predict potential adverse events, and recommend therapy management guidance to the host to prevent the adverse health events from occurring without requiring hospital readmission.

[0081] In certain embodiments, the sensor electronics module 204 includes a processor 233, a storage element or memory 234, a wireless transmitter / receiver (transceiver) 236, one or more antennas coupled to wireless transceiver 236, analog electrical signal processing circuitry, analog to-digital (A / D) signal processing circuitry, digital signal processing circuitry, a power source for continuous analyte sensor(s) 202 (such as a potentiostat), etc.

[0082] The processor 233 may be a general-purpose or application- specific microprocessor, an application- specific integrated circuit (ASIC), a field programmable gate array (FPGA), etc., that executes instructions to perform control, computation, input / output, etc. functions for the sensor electronics module 204. The processor 233 may include a single integrated circuit, such as a micro processing device, or multiple integrated circuit devices and / or circuit boards working in cooperation to accomplish the appropriate functionality. In certain embodiments, the processor 233, the memory 234, the wireless transceiver 236, the A / D signal processing circuitry, and the digital signal processing circuitry may be combined into a system-on-chip (SoC).

[0083] Generally, the processor 233 may sample the analog electrical signal using the A / D signal processing circuitry at regular intervals (such as the sampling period) to generate analyte sensor count values based on the analog electrical signals produced by the continuous analyte sensor(s) 202, calibrate the analyte sensor count values based on the sensitivity profile of the continuous analyte sensor(s) 202 to generate measured analyte concentration levels, and generate measured analyte data from the measured analyte concentration levels, generate sensor data packages that include, inter alia, the measured analyte concentration level data. The processor 233 may store the measured analyte concentration level data in the memory 234and generate the sensor data packages at regular intervals (such as the transmission period) for transmission by the wireless transceiver 236 to a display device, such as display devices 210, 220, 230, and / or 240. The processor 233 may also add additional data to the sensor data packages, such as supplemental sensor information that includes a sensor identifier, a sensor status, temperatures that correspond to the measured analyte data, etc. The sensor data packages are then wirelessly transmitted over a wireless connection to the display device. In certain embodiments, the wireless connection is a Bluetooth or Bluetooth Low Energy (BLE) connection. In such embodiments, the sensor data packages are transmitted in the form of Bluetooth or BLE data packets to the display device

[0084] The memory 234 may include volatile and nonvolatile media. For example, the memory 234 may include combinations of random-access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), read only memory (ROM), flash memory, cache memory, and / or any other type of non-transitory computer-readable medium. The memory 234 may store one or more analyte sensor system applications, modules, instruction sets, etc. for execution by the processor 233, such as instructions to generate measured analyte data from the analyte sensor count values, etc.

[0085] The memory 234 may also store certain sensor operating parameters 235, such as a calibration slope (or calibration sensitivity), a calibration baseline, etc. In particular, the calibration sensitivity, calibration baseline, and other information related to the sensitivity profile for the sensor electronics module 204 may be programmed into the sensor electronics module 204 during the manufacturing process, and then used to convert the analyte sensor electrical signals into measured analyte concentration levels. For example, the calibration slope may be used to predict an initial in vivo sensitivity (Mo) and a final in vivo sensitivity (Mf), which are stored in the memory 234 and used to convert the analyte sensor electrical signals into measured analyte concentration levels. The calibration sensitivity (Mcc) 246 and / or calibration baseline 247 may also be stored in the memory 234.

[0086] The sensor electronics module 204 includes electronic circuitry associated with measuring and processing the continuous analyte sensor data, including prospective algorithms associated with processing and calibration of the sensor data. The sensor electronics module 204 can be physically connected to the continuous analyte sensor(s) 202 and can be integral with (non-releasably attached to) or releasably attachable to the continuous analyte sensor(s)202. The sensor electronics module 204 may include hardware, firmware, and / or software that enable measurement of levels of analyte(s) via continuous analyte sensor(s) 202. For example, the sensor electronics module 204 can include a potentiostat, a power source for providing power to the sensor, other components useful for signal processing and data storage, and a telemetry module for transmitting data from the sensor electronics module to, e.g., one or more display devices. Electronics can be affixed to a printed circuit board (PCB), or the like, and can take a variety of forms. For example, the electronics can take the form of an integrated circuit (IC), such as an Application-Specific Integrated Circuit (ASIC), a microcontroller, and / or a processor.

[0087] The display devices 210, 220, 230, and / or 240 display displayable sensor data, including analyte data, which may be transmitted by the sensor electronics module 204. Each of the display devices 210, 220, 230, or 240 may include a display such as a touchscreen display 212, 222, 232, and / or 242 for displaying sensor data to a host and / or for receiving inputs from the host. For example, a graphical user interface (GUI) may be presented to the host for such purposes. In certain embodiments, the display devices may include other types of user interfaces such as a voice user interface instead of, or in addition to, a touchscreen display for communicating sensor data to the host of the display device and / or for receiving host inputs. The display devices 210, 220, 230, and 240 may be examples of the display device 107 illustrated in FIG. 1 used to display sensor data to a host of the system of FIG. 1 and / or to receive input from the host.

[0088] In certain embodiments, one, some, or all of the display devices display or otherwise communicate (e.g., verbalize) the sensor data as it is communicated from the sensor electronics module (e.g., in a customized data package that is transmitted to display devices based on their respective preferences), without any additional prospective processing required for calibration and real-time display of the sensor data.

[0089] The display devices 210, 220, 230, and / or 240 may display received sensor data at a particular display rate, such as every 1 second, 5 seconds, 10 seconds, 30 seconds, 1 minute, 3 minutes, 5 minutes, 60 minutes, 4 hours, 8 hours, 12 hours, 24 hours, etc. The display rate of the display devices 210, 220, 230, and / or 240 may be less than, substantially equal to, or longer than the sampling period (or rate) and / or transmission period (or rate) of the sensor electronics module 204. Generally, the display devices 210, 220, 230, and / or 240 can displayindividual sensor measurements, measurement trends, and / or aggregate measurement statistics such as hourly, daily, weekly, and monthly aggregate values and deviations from trends.

[0090] The display devices may include a custom display device specially designed for displaying certain types of displayable sensor data associated with analyte data received from the sensor electronics module. In certain embodiments, the display devices may provide alerts / alarms based on the display able sensor data. The display device 210 is an example of such a custom device. In certain embodiments, one of the display devices is a smartphone, such as the display device 220, which represents a mobile phone, using a commercially available operating system (OS), and configured to display a graphical representation of the continuous sensor data (e.g., including current and historic data). Other display devices can include other hand-held devices, such as the display device 230, which represents a tablet or phablet, the display device 240, which represents a smart watch or fitness tracker, the medical device 208 (e.g., a medication administration device or a blood glucose meter), and / or a desktop or laptop computer (not shown).

[0091] Because different display devices provide different user interfaces, content of the data packages (e.g., amount, format, and / or type of data to be displayed, alarms, and the like) can be customized (e.g., programmed differently by the manufacture and / or by an end host) for each particular display device. Accordingly, in certain embodiments, different display devices can be in direct wireless communication with a sensor electronics module (e.g., such as an on- skin sensor electronics module 204 that is physically connected to continuous analyte sensor(s) 202) during a sensor session to enable a plurality of different types and / or levels of display and / or functionality associated with the displayable sensor data.

[0092] The sensor electronics module 204 may be in communication with the medical device 208. The medical device 208 may be a passive device. For example, the medical device 208 may be a medication pump for administering one or more medications to a host, such as one or more medications for treating diabetes. For a variety of reasons, it may be desirable for such a medication pump to receive and track potassium, lactate, glucose, calcium, creatinine, and / or other analytes transmitted from the continuous analyte monitoring systems, where the continuous analyte sensor 202 measures glucose, lactate, calcium, creatinine, and / or other analytes. In certain embodiments, the medical device 208 may include an insulin pump.

[0093] Further, as mentioned, the sensor electronics module 204 may also be in communication with other non-analyte sensors 206. The non-analyte sensors 206 may include, but are not limited to, an altimeter sensor, an accelerometer sensor, a global positioning system (GPS) sensor, a temperature sensor, a respiration rate sensor, etc. The non-analyte sensors 206 may also include monitors such as heart rate monitors, blood pressure monitors, pulse oximeters, cardiovascular implantable electronic devices (CIEDs) such as implantable cardioverter defibrillators (ICDs), pacemakers (PMs), cardiac resynchronization therapy (CRT) devices, implantable loop recorders (ILRs), and implantable hemodynamic monitors (IHMs), caloric intake monitors, indirect calorimetry devices and medicament administration / delivery devices. One or more of these non-analyte sensors 206 may provide data to the management engine 114 described further below. In some aspects, a host may manually provide some of the data for processing by the training server system 140 and / or the management engine 114 of FIG. 1. The non-analyte sensors 206 may further include sensors for measuring skin temperature, core temperature, sweat rate, and / or sweat composition.

[0094] In certain embodiments, the non-analyte sensors 206 may be combined in any other configuration, such as, for example, combined with one or more continuous analyte sensors 202. As an illustrative example, a non-analyte sensor, e.g., a temperature sensor, may be combined with a continuous analyte sensor 202 to form an analyte / temperature sensor used to transmit sensor data to the sensor electronics module 204 using common communication circuitry. As another illustrative example, a non-analyte sensor, e.g., a temperature sensor, may be combined with a multi-analyte sensor 202 to measure glucose and / or lactate to form a glucose / lactate / temperature sensor used to transmit sensor data to the sensor electronics module 204 using common communication circuitry.

[0095] In certain embodiments, a wireless access point (WAP) may be used to couple one or more of the continuous analyte sensor system 104, the display devices, the medical device(s) 208, and / or the non-analyte sensor(s) 206 to one another. For example, the WAP 138 may provide Wi-Fi and / or cellular connectivity among these devices. Near Field Communication (NFC) and or Bluetooth may also be used among devices depicted in diagram 200 of FIG. 2.

[0096] FIG. 3 illustrates example inputs and example metrics that are calculated based on the inputs for use by the analyte monitoring system 100 of FIG. 1, according to someembodiments disclosed herein. In particular, FIG. 3 provides a more detailed illustration of example inputs and example metrics introduced in FIG. 1.

[0097] FIG. 3 illustrates example inputs 130 on the left, the application 106 and the DAM 116 in the middle, and the metrics 132 on the right. In certain embodiments, each one of the metrics 132 may correspond to one or more values, e.g., discrete numerical values, ranges, or qualitative values (high / medium / low, stable / unstable, etc.). The application 106 obtains inputs 130 through one or more channels (e.g., manual host input, sensors, other applications executing on display device 107, an EMR system, etc.). As mentioned previously, in certain embodiments, the inputs 130 may be processed by the DAM 116 to output a plurality of metrics, such as the metrics 132. The inputs 130 and the metrics 132 may be used by the training server system 140 and the management engine 114 to both train and deploy one or more machine learning models for determining disease progression, providing therapy management guidance or treatment, and other functionalities described herein.

[0098] Starting with the inputs 130, host statistics, such as one or more of age, height, weight, BMI, body composition (e.g., % body fat or % muscle from a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, dual-energy X-ray absorptiometry (DEXA) scan, etc.), stature, build, or other information may also be provided as an input. In certain embodiments, the host statistics are provided through a user interface, by interfacing with an electronic source such as an electronic medical record, and / or from measurement devices. In certain embodiments, the measurement devices include one or more wireless devices, e.g., Bluetooth-enabled, weight scale and / or camera, which may, for example, communicate with the display device 107 to provide the host data.

[0099] The medication / treatment information may also be provided as an input. Medication information may include information about the type, dose, and / or timing of when one or more medications are to be taken by the host. As mentioned elsewhere herein, the medication information may include information about one or more medications prescribed to the host for treating one or more symptoms of cardiovascular disease (e.g., HF), kidney disease, diabetes, and / or other conditions. In certain embodiments, the medication information includes information about administration of insulin, dextrose, and / or glucose, and / or other drugs prescribed for treatment of acute hyper- and / or hypokalemia or other acute conditions. Treatment information may further include information regarding different lifestyle habits,surgical procedures, dialysis, and / or other invasive or non-invasive procedures recommended by the host’s physician. For example, the host’s physician may recommend a user increase / decrease their potassium intake, or exercise for a minimum of thirty minutes a day, to reduce hyper- and / or hypokalemic episodes, etc. The medic ation / treatment information may be provided through manual host input.

[0100] In certain embodiments, the analyte sensor data may also be provided as input, for example, through the continuous analyte sensor system 104. The analyte sensor data may include analyte data measured by at least an analyte sensor (or multi-analyte sensor). For example, the analyte sensor data may include glucose data measured by at least a glucose sensor (or multi-analyte sensor) in the continuous analyte sensor system 104. For hosts undergoing intensive insulin therapy, glucose data can be used as a predictor of how and when the host would be dosing insulin, and such data may then also be used as an input 130 to forecast other metrics. In certain embodiments, the analyte sensor data may include lactate data measured by at least a lactate sensor (or multi-analyte sensor) in the continuous analyte sensor system 104. The analyte sensor data may include other analyte data, such as calcium data, creatinine data, BUN data, ammonia data, C-peptide data, or cystatin C-data, measured by a sensor (or multianalyte sensor) in the continuous analyte sensor system 104.

[0100] The input may also be received from one or more non-analyte sensors, such as nonanalyte sensors 206 described with respect to FIG. 2. Input from such non-analyte sensors 206 may include information related to heart rate, heart rate variability, electrocardiogram (ECG) data, respiration rate, oxygen saturation, blood pressure, blood volume, blood volume / host weight, accelerometer data, urine output, or a body temperature (e.g., to detect illness, physical activity, etc.) of a host. In certain embodiments, electromagnetic sensors may also detect low- power radio frequency (RF) fields emitted from objects or tools touching or near the object, which may provide information about host activity or location.

[0101] The input received from non-analyte sensors may include input relating to a user’s medication administration / delivery. In particular, input related to the user’s medication administration may be received, via a wireless connection on a smart pen, via user input, and / or from a medication pump or other device. Medication administration information may include one or more of medication volume, time of delivery, etc. Other parameters, such as medication action time or duration of medication action, may also be received as inputs.

[0102] The inputs 130 may also include food consumption information, including information about one or more of meals, snacks, and / or beverages, such as one or more of the size, content (carbohydrate, fat, protein, etc.), sequence of consumption, and time of consumption. The food consumption information may be provided by a host through manual entry, by providing a photograph through an application that is configured to recognize food types and quantities, and / or by scanning a bar code or menu. In various examples, meal size may be manually entered as one or more of calories, quantity (“three cookies”), menu items (“Royale with Cheese”), and / or food exchanges (1 fruit, 1 dairy). In some examples, meal information may be received via a convenient user interface provided by the application 106.

[0103] The food consumption information (the type of food (e.g., liquid or solid, snack or meal, etc.) and / or the composition of the food (e.g., carbohydrate, fat, protein, etc.)) may be determined automatically based on information provided by one or more sensors. Some example sensors may include body sound sensors (e.g., abdominal sounds may be used to detect the types of meal, e.g., liquid / solid food, snack / meal, etc.), radio-frequency sensors, cameras, hyperspectral cameras, and / or analyte (e.g., potassium, insulin, glucose, lactate, calcium, creatinine, etc.) sensors to determine the type and / or composition of the food.

[0104] The input 130 may include sleep information for a user. For example, the sleep information may indicate times when the user went to sleep and times when the user woke up. The sleep information may also indicate sleep quality or locations where the user slept. This information may be used in conjunction with other inputs 130 to determine a fasting glucose level for the user.

[0105] Medical history and / or disease diagnoses (e.g., cardiovascular disease, kidney disease, diabetes, liver disease, hypertension, etc.) may be provided as an input. For example, the host may have an existing diagnosis of diabetes and this diagnosis may be provided through manual host input. In certain embodiments, disease diagnoses are also provided by interfacing with an electronic source such as an EMR.

[0106] Exercise / activity information may also be provided as an input. Exercise information may be any information surrounding activities requiring physical exertion by the host. For example, exercise information may range from information related to low intensity (e.g., walking a few steps) and high intensity (e.g., five-mile run) physical exertion. In certain embodiments, the exercise information may include information related to high intensityinterval training (HIIT), resistance training, or Zone 2 training. Exercise information may also be provided through manual host input suggesting the host will begin a specific exercise type and / or with certain exercise parameters. The exercise information may be provided or determined based on information provided, for example, by non-analyte sensors 206 (e.g., a temperature sensor, a heart rate monitor, a wearable blood pressure monitor, an accelerometer sensor on a wearable device such as a watch, fitness tracker, and / or patch, etc.). The exercise information may be provided or determined based on information provided, for example, by continuous analyte sensor system 104 (e.g., it may be deduced that the host engaged in exercise based on their glucose, lactate, and / or ketone data). The exercise information provided by analyte and non-analyte sensors may be used as input into a model trained for predicting whether the host is engaging in exercise and / or predicting the types and / or parameters of such exercise.

[0107] Environmental information may be provided as an input. The environmental information may include altitude, atmospheric pressure, weather, and / or environmental temperature (indoor and / or outdoor) information, as such environmental factors can influence metabolic activity and cause metabolic variability of the host.

[0108] Date / time information may also be provided as an input. The date and / or time information may be processed by the DAM 116 independently of other inputs 130, or may be dependent upon (e.g., associated with) another input 130. Time information may include time of day or time from a real-time clock. For example, in certain embodiments, input analyte data may be timestamped to indicate a date and time when the analyte measurement was taken for the host.

[0109] Information related to pregnancy may also be provided as an input. For example, the pregnancy information may include pregnancy status and expected due date for delivery. The pregnancy information may be provided through manual host input.

[0110] Host input of any of the above-mentioned inputs 130 may be provided through continuous analyte sensor system 104, non-analyte sensors 206, and / or a user interface, such a user interface of display device 107 of FIG. 1. As described above, in certain embodiments, the DAM 116 determines or computes the host’s metrics 132 based on inputs 130. An example list of metrics 132 is shown in FIG. 3.

[0111] In certain embodiments, analyte metrics (e.g., glucose metrics) may be calculated by the DAM 116 based on the inputs 130. Analyte metrics may include analyte levels, analyte baselines, maximum and minimum analyte levels, analyte rates of change, and / or analyte baseline rates of change.

[0112] In certain embodiments, analyte levels may be determined from sensor data (e.g., glucose measurements obtained from a CPM of continuous analyte sensor system 104). For example, analyte levels refer to time-stamped analyte measurements or values that are continuously generated and stored over time.

[0113] In certain embodiments, an analyte baseline may be determined from sensor data (e.g., analyte measurements obtained from the continuous analyte sensor system 104). An analyte baseline represents a host’s normal analyte levels during periods where significant fluctuations in analyte levels are typically not expected. A host’s analyte baseline is generally expected to remain constant or within a narrow “normal range” over time, unless challenged through an action such as by the consumption of foods (e.g., diet), performance of exercise, or administration of a medicament (e.g., insulin). Generally, increasing fluctuation from the host’s analyte baseline may indicate a loss of analyte regulation (e.g., glycemic control), which may put the host at an increased risk.

[0114] Further, each host may have a different potassium baseline. In certain embodiments, a host’s potassium baseline may be determined by calculating an average of potassium levels of the user over a specified amount of time where significant fluctuations are not expected. For example, the baseline potassium for a host may be determined over a period of time when the host is sleeping, sitting in a chair, or other periods of time where the host is sedentary and not consuming food or medication which would reduce or increase potassium levels (e.g., where no external conditions exist that would affect the potassium baseline exist). In certain embodiments, DAM 116 may continuously calculate a potassium baseline, timestamp the calculated potassium baseline, and store the corresponding information in the host’s profile 118.

[0115] In certain embodiments, an absolute maximum analyte level may be determined from sensor data (e.g., analyte measurements obtained from a CPM of continuous analyte monitoring system 104), health / sickness metrics (e.g., described in more detail below), and / or disease stage metrics (e.g., described in more detail below). The absolute maximum analytelevel represents a host’s maximum analyte level determined to be safe over a period of time (e.g., hourly, weekly, daily, etc.). In certain embodiments, the absolute maximum analyte level may be consistent across all hosts (e.g., based on current medical guidelines). In certain other embodiments, each host may have a different absolute maximum analyte level. In certain embodiments, the absolute maximum analyte level per host may change over time. For example, a host may be initially assigned an absolute maximum analyte level based on clinical input. This assigned absolute maximum analyte level may be adjusted over time based on other sensor data, disease stages, comorbidities, etc. for the host.

[0116] For example, a host’s absolute maximum analyte level may vary over time as a user’s disease progresses and / or improves. In certain embodiments, a first absolute maximum analyte level may be determined for periods of time where no external conditions exist that would affect the analyte level, and a second absolute maximum analyte level may be determined for periods of time where external conditions do exist that would affect the analyte level (e.g., during periods of time when the host is eating, exercising, taking medication that affects analyte levels, etc.).

[0117] In certain embodiments, an absolute minimum analyte level may be determined from sensor data (e.g., analyte measurements obtained from a CPM of continuous analyte monitoring system 104), medication / treatment metrics, and / or medical history / disease diagnosis metrics. The absolute minimum analyte level represents a host’s minimum analyte level determined to be safe over a period of time (e.g., hourly, weekly, daily, etc.). In certain embodiments, the absolute minimum analyte level may be consistent across all hosts (e.g., set based on current medical guidelines). In certain other embodiments, each host may have a different absolute minimum analyte level. In certain embodiments, the absolute minimum analyte level per host may change over time. For example, a host may be initially assigned an absolute minimum analyte level based on clinical input. This assigned absolute minimum analyte level may be adjusted over time based on other sensor data, disease stages, comorbidities, etc. for the host.

[0118] For example, a host’s absolute minimum analyte level may vary over time as a host’s diseases progress and / or improves. In certain embodiments, a first absolute minimum analyte level may be determined for periods of time where no external conditions exist that would affect the analyte level, and a second absolute minimum analyte level may be determinedfor periods of time where external conditions do exist that would affect the analyte level (e.g., during periods of time when the host is eating, exercising, taking medication that affects analyte levels, etc.).

[0119] In certain embodiments, analyte level rates of change may be determined from sensor data (e.g., analyte measurements obtained from a CPM of continuous analyte monitoring system 104 over time). For example, an analyte level rate of change refers to a rate that indicates how one or more time- stamped analyte measurements or values change in relation to one or more other time-stamped analyte measurements or values. Analyte level rates of change may be determined over one or more seconds, minutes, hours, days, etc.

[0120] In certain embodiments, determined analyte level rates of change may be marked as “increasing rapidly” or “decreasing rapidly.” As used herein, “rapidly” may describe analyte level rates of change that are clinically significant and pointing towards a trend of the analyte level of the patient likely breaching the absolute maximum analyte level or the absolute minimum analyte level within a next period of defined time. In other words, a predictive trend (e.g., produced by the management engine 114 using one or more trained models) may, in some cases, indicate that a host is likely to hit, for example, the absolute maximum analyte level within a specified time period (e.g., one or two hours) based on the determined analyte level rate of change. Accordingly, such an analyte level rate of change may be marked as “increasing rapidly.” Similarly, a predictive trend (e.g., produced by the management engine 114 using one or more trained models) may, in some cases, indicate that a host is likely to hit the absolute minimum analyte level within a specified time period (e.g., one or two hours) based on the analyte level rate of change determined. Accordingly, such an analyte level rate of change may be marked as “decreasing rapidly.”

[0121] In certain embodiments, analyte baseline rates of change may be determined from analyte baselines determined for a host over time. For example, an analyte baseline rate of change refers to a rate that indicates how one or more time-stamped analyte baselines for a host change in relation to one or more other time-stamped analyte baselines for the same host. Analyte baseline rates of change may be determined over one or more seconds, minutes, hours, days, etc.

[0122] The host’s metrics 132 may further include metrics for other analytes. For example, in certain embodiments, metrics 132 may include lactate levels, lactate baselines,maximum and minimum lactate levels, lactate rates of change, lactate baseline rates of change, lactate clearance rates, lactate trends, calcium levels, calcium baselines, maximum and minimum calcium levels, calcium rates of change, calcium baseline rates of change, calcium clearance rates, calcium trends, ketone levels, ketone baselines, maximum and minimum ketone levels, ketone rates of change, ketone baseline rates of change, ketone clearance rates, ketone trends, creatinine levels, creatinine baselines, maximum and minimum creatinine levels, creatinine rates of change, creatinine baseline rates of change, creatinine clearance rates, creatinine trends, and / or levels, baselines, maximum and minimum levels, rates of change, baseline rates of change, clearance rates, and / or trends of one or more other analytes of the host.

[0123] In certain embodiments, meal state metrics may indicate the state the host is in with respect to food consumption. For example, the meal state may indicate whether the host is in one of a fasting state, pre-meal state, eating state, post-meal response state, or stable state. In certain embodiments, the meal state may also indicate nourishment on board, e.g., meals, snacks, or beverages consumed, and may be determined, for example from food consumption information, time of meal information, and / or digestive rate information, which may be correlated to food type, quantity, and / or sequence (e.g., which food / beverage was eaten first).

[0124] In certain embodiments, meal habits metrics are based on the content and the timing of a host’s meals. For example, if a meal habit metric is on a scale of 0 to 1, the better / healthier meals the host eats the higher the meal habit metric of the host will be to 1, in an example. Also, the more the host’s food consumption adheres to a certain time schedule or a recommended diet, the closer their meal habit metric will be to 1, in the example.

[0125] In certain embodiments, body temperature metrics may be calculated by the DAM 116 based on inputs 130, and more specifically, non-analyte sensor data from a temperature sensor. Heart rate metrics (e.g., including heart rate and heart rate variability) may be calculated by the DAM 116 based on inputs 130, and more specifically, non-analyte sensor data from a heart rate sensor. Respiratory metrics may be calculated by the DAM 116 based on inputs 130, and more specifically, non-analyte sensor data from a respiratory rate sensor.

[0126] Health and sickness metrics may be determined, for example, based on one or more of host input (e.g., pregnancy information or known sickness information), from physiologic sensors (e.g., temperature), activity sensors, or a combination thereof. In certain embodiments,based on the values of the health and sickness metrics, for example, a host’s state may be defined as being one or more of healthy, ill, rested, or exhausted.

[0127] Medication habit metrics are based on the host’s prescribed medications and a determination of whether the prescribed medications may have an effect on the host’s analyte levels. For example, by analyzing a host’s medication habits, the DAM 116 may determine whether the host’s medications may impact the host’s analyte measurements at a particular time. Based on the host’s medication habits, the DAM 116 may determine whether the host’s analyte levels are a result of medication consumption or another cause, such as worsening organ function, for example. The medication habit metrics may be time-stamped so that they can be correlated with the host’s analyte levels at the same time.

[0128] Treatment habit metrics are based on the host’s lifestyle habits, surgical procedures, and / or other non-invasive procedures recommended by the host’s physician, and a determination of whether the treatment habits may have an effect on the host’s analyte levels. For example, by analyzing a host’s treatment habits, the DAM 116 may determine whether the host’s treatment habits may impact the host’s analyte measurements at a particular time. Based on the host’s treatment habits, the DAM 116 may determine whether the host’s analyte levels are a result of treatment habits or another cause. The treatment habit metrics may be time- stamped so that they can be correlated with the host’s analyte levels at the same time.

[0129] Medication adherence is measured by one or more metrics that are indicative of how committed the host is towards their medication regimen. In certain embodiments, medication adherence metrics are calculated based on one or more of the timing of when the host takes medication (e.g., whether the host is on time or on schedule), the type of medication (e.g., is the host taking the right type of medication), and the dosage of the medication (e.g., is the host taking the right dose).

[0130] Similarly, treatment adherence is measured by one or more metrics that are indicative of how committed the host is towards their treatment regimen. In certain embodiments, treatment adherence metrics are calculated based on one or more of the timing of when the host performs certain treatment habits and / or the type of treatment.

[0131] The activity level metric may indicate the host’s level of activity. In certain embodiments, the activity level metric be determined, for example based on input from an activity sensor or other physiologic sensors, such as non-analyte sensors 206. In certainembodiments, the activity level metric may be calculated by the DAM 116 based on one or more of inputs 130, such as one or more of exercise information, non-analyte sensor data (e.g., accelerometer data), time, host input, etc. In certain embodiments, the activity level may be expressed as a step rate of the host. Activity level metrics may be time-stamped so that they can be correlated with the host’s potassium levels at the same time.

[0132] The analyte monitoring system is designed with advanced technical features that provide comprehensive and continuous assessment of a user’s glycemic control. At its core, the system employs a continuous glucose monitor (CGM) or a multi-analyte sensor to collect time-stamped glucose measurements from the user in real time. This sensor data is complemented by contextual inputs such as meal times, food composition, sleep patterns, exercise or activity levels, medication administration (e.g., insulin and non-insulin glucose- lowering medications), and other physiological status indicators. These contextual data points are gathered through user input, integration with connected devices, or additional sensors, providing a holistic view of the user’s daily life and health behaviors.

[0133] The system’s operation begin with the continuous collection of glucose data, which may be automatically transmitted to the monitoring platform. Contextual data — such as when the user eats, sleeps, or exercises — may be logged either manually by the user or automatically detected by the system. When available, manual inputs may be validated by cross-referencing device logs (e.g., health trackers, sleep sensors, or smartwatches) to ensure accuracy. The monitoring system then identifies relevant events, such as periods of fasting (e.g., overnight or before breakfast) or post-meal windows, by analyzing time stamps and contextual information. This event identification is crucial for accurate metric calculation, as it allows the system to distinguish between fasting glucose levels and post-prandial (after meal) glucose responses. If overlapping events do occur (e.g., a snack during a fasting window), the system may apply predefined rules to prioritize or exclude conflicting data points.

[0134] After events are identified, the system calculates a range of clinically relevant glycemic metrics. For example, fasting glucose may be determined by averaging glucose readings during a fasting window, such as the two hours before and after waking. Post-prandial glucose may be calculated as the peak or average glucose value within two to three hours after a logged meal. If a meal is not logged, the system may infer meal timing based on glucose spikes and activity patterns, though these estimates may be flagged as “lower-confidence.” Thesystem may also compute mean glucose over a defined period (such as seven or fourteen days), time-in-range (TIR) — which may be the percentage of readings within the target range of 70- 180 mg / dL — and glycemic variability, typically expressed as the coefficient of variation (%CV) of glucose readings. Additionally, the system may track the number of hypoglycemic events (e.g., readings below 70 mg / dL or 54 mg / dL) and hyperglycemic events (e.g., readings above 180 mg / dL or 250 mg / dL) over specified timeframes.

[0135] The system may calculate a set of core glycemic metrics over defined periods (e.g., daily, weekly, or biweekly). These metrics may include:• Fasting glucose: The average glucose value during fasting windows.• Post-prandial glucose: The peak or average glucose value within two to three hours after a meal. Alternatively, the incremental area under the curve from 3 to 5 hours postprandial can be defined as the area above the glucose level measured at meal onset.• Mean glucose: The average of glucose readings over a set period (e.g., 14 days).• Time-in-range (TIR): The percentage of glucose readings within the target range, 70-180 mg / dL.• Time-in-tight-range (TITR): The percentage of glucose readings within 70-140 mg / dL, which may be more indicative for health and prediabetes users.• Glycemic variability: The coefficient of variation (%CV) of glucose readings, reflecting fluctuations.• Number of hypoglycemic and hyperglycemic events: Counts of readings below or above clinically significant thresholds. This may include level 1 hypoglycemia (<70 mg / dL), level 2 hypoglycemia (<54 mg / dL), level 1 hyperglycemia (>180 mg / dL), and level 2 hyperglycemia (>250 mg / dL).• Duration of hypoglycemic events: Contiguous time (e.g., minutes or hours) spent in hypoglycemia levels.

[0136] To interpret these metrics, the system may compare them to clinical thresholds and apply a set of rules to assess glycemic control. For instance, if the average fasting glucose over the past seven days is greater than or equal to 126 mg / dL, the system may flag this measurement as “poor fasting control.” If post-prandial glucose exceeds 180 mg / dL in more than 30% ofmeals during the past week, the system may mark this measurement as “suboptimal postprandial control.” Similarly, if the mean glucose over fourteen days is above 154 mg / dL, or if time-in-range falls below 70%, the system may identify these as “elevated average glucose” and “insufficient time in range,” respectively. High glycemic variability is flagged if %CV exceeds 36%, while frequent hypoglycemic or hyperglycemic events are also highlighted based on predefined counts within a week.

[0137] For example, consider a user with type 1 diabetes whose recent data shows a fasting glucose average of 132 mg / dL, post- prandial glucose averaging 185 mg / dL, mean glucose of 162 mg / dL, TIR of 65%, %CV of 38%, one hypoglycemic event below 54 mg / dL, and six hyperglycemic events above 250 mg / dL in the past week. The system may flag poor fasting control, suboptimal post-prandial control, elevated average glucose, insufficient time in range, high glycemic variability, and increased hyperglycemia risk, while noting that hypoglycemia is within acceptable limits. Synthesizing these findings, the system may determine the user’s overall glycemic control level as “suboptimal” or “needs improvement,” and may generate specific recommendations for therapy adjustment or lifestyle intervention.

[0138] The system may synthesize the results of all these rules to assign an overall glycemic control level, such as “optimal,” “suboptimal,” or “poor.” This overall assessment may be based on a weighted combination of the individual metrics, the number and severity of rule violations, or a scoring system that reflects the user’s risk profile. The calculated glycemic control level is then reported to the user or healthcare provider, often accompanied by specific recommendations for therapy adjustment, lifestyle changes, or further clinical evaluation.

[0139] The analyte monitoring system may use a weighted approach to both the calculation of the overall glycemic control level and the prioritization of recommendations, which may allow the most clinically significant factors to exert the greatest influence on the final assessment and advice. In this framework, each glycemic metric — such as severe hypoglycemia events, time-in-range (TIR), mean glucose, glycemic variability, fasting glucose, and post-prandial glucose — may be assigned a specific weight that reflects its relative importance to patient safety and long-term health outcomes. For example, severe hypoglycemia, due to its immediate risk, may be given the highest weight, while metrics like mean glucose and TIR receive moderate weights, and less critical factors such as mild postprandial excursions are weighted lower. The system may calculate a composite glycemiccontrol score by multiplying each metric’s normalized value by its assigned weight and summing the results, which is then mapped to categories such as “optimal,” “suboptimal,” or “poor” glycemic control. Initial weights may be based on clinical guidelines, but the system may also adjust them dynamically using machine learning algorithms that take into consideration user outcomes, genetic medicine, population health, and provider feedback.

[0140] The weighted scoring may not only determine the user’s overall glycemic control level, but also may guide the prioritization of recommendations. If the composite score is most heavily influenced by frequent hypoglycemia, the system may prioritize urgent, safety-focused interventions — such as medication dose adjustments or immediate provider contact — over less urgent advice like dietary modifications for mild post-meal spikes. The weighting scheme may be dynamically adjusted based on user-specific factors, such as age, sex, genetics, social determinants of health, comorbidities, pregnancy status, or recent clinical events, and may also incorporate provider input or user preferences. Over time, the system may adapt these weights using machine learning, learning from user outcomes and responses to previous recommendations. This approach ensures that the most pressing clinical issues are addressed first, supports transparent and explainable decision-making, and allows the system to remain responsive to evolving clinical guidelines and individual patient needs.

[0141] The advantages of this approach may be significant. By automating the assessment of glycemic control, the system may reduce subjectivity and human error, providing objective, data-driven insights. The use of personalized thresholds and rules allows the system to tailor its analysis to individual patient profiles, such as diabetes type, pregnancy status, or comorbidities. The system’s ability to track trends over time enables proactive management, alerting users and healthcare providers to deteriorating control before complications arise. As described herein, metrics, parameters, and corresponding rates of change of velocity may be tracked to better understand the efficacy of therapeutic and behavioral interventions. Alternatively, the system may incorporate predictive analytics to forecast glycemic trends and alert users to potential risks before they occur. Ultimately, this integrated, rule-based, and metric-driven process supports precise, personalized, and actionable diabetes management, empowering users to make informed decisions and clinicians to optimize care.

[0142] The analyte monitoring system determines specific recommendations for therapy adjustment or lifestyle intervention by first analyzing the user’s glycemic control metrics inrelation to established clinical thresholds and then mapping any identified issues to evidencebased interventions. After calculating key metrics such as fasting glucose, post-prandial glucose, mean glucose, time-in-range, glycemic variability, and the frequency of hypo- or hyperglycemic events, the system may compare these values to clinical or personalized targets. When a metric falls outside the optimal range — such as fasting glucose consistently above 126 mg / dL, post-prandial glucose frequently exceeding 180 mg / dL, or time-in-range dropping below 70% — the system may flag these as specific problem areas, for example, “poor fasting control” or “suboptimal post-prandial control.”

[0143] The system may synthesize these findings to determine an overall glycemic control level, such as “optimal,” “suboptimal,” or “poor.” This overall control level serves as a key driver for the type and urgency of recommendations provided. For example, if the user’s glycemic control is assessed as “optimal,” the system may simply reinforce current behaviors, provide positive feedback, and suggest routine monitoring. If the control level is “suboptimal,” the system identifies which specific metrics are problematic — such as elevated fasting glucose, frequent post-prandial spikes, or low time-in-range — and maps these to targeted interventions. For instance, persistent high fasting glucose may prompt recommendations to review evening carbohydrate intake, adjust basal insulin or long-acting medication, or improve sleep hygiene. Frequent post-prandial excursions could lead to advice on modifying meal composition, adjusting pre-meal medication timing or dose, or incorporating post-meal physical activity. If the glycemic control level is “poor,” especially with frequent hypoglycemia or hyperglycemia, the system prioritizes safety-focused interventions, such as urgent medication review, immediate dietary adjustments, or scheduling a prompt consultation with a healthcare provider.

[0144] To provide relevant and personalized recommendations, the system contextualizes these flagged issues using the user’s profile and recent behaviors. It takes into account factors such as diabetes type, age, sex, genetics, social determinants of health, provider input, user preferences, pregnancy status, comorbidities, recent medication changes, adherence patterns, meal timing and composition, physical activity, sleep quality, and stress levels. This comprehensive view allows the system to tailor its advice to the individual’s unique clinical situation and lifestyle.

[0145] For each identified problem, the system references a knowledge base of clinical guidelines and best practices, and may also leverage machine learning models trained onpopulation and individual data. The system may prioritize these recommendations based on the severity and frequency of the flagged issues, user preferences, and clinical urgency, allowing the most critical interventions to be addressed first. Recommendations may then be delivered to the user, and optionally to their healthcare provider, through the device interface, app, or secure messaging. The system may also provide educational resources, reminders, or prompts for follow-up actions. Over time, it tracks the user’s response to these recommendations and adapts future advice accordingly, creating a dynamic, personalized, and evidence-based approach to diabetes management that supports both immediate safety and long-term health outcomes.

[0146] The DAM 116 may determine a glycemic control level for a host by considering the inputs 130 and / or the analyte metrics over time. For example, by tracking the analyte levels (e.g., glucose levels) for the host over a period of time, the DAM 116 may determine how well the host is controlling the host’s glucose levels. The DAM 116 or management engine 114 may determine the glycemic control level using other factors, such as the disease (e.g., diabetes) progression of the host, the pregnancy status and / or due date for the host, the meal times for the host, the exercise times for the host, etc. In this manner, the DAM 116 or management engine 114 may accurately determine the glycemic control level for each host over time. The management engine 114 may then administer particular medical treatments for the disease progression.

[0147] FIG. 4 illustrates an example operation 400 for determining and administering a treatment performed by an analyte monitoring system (e.g., the analyte monitoring system 100 of FIG. 1). Generally, one or more components of the analyte monitoring system (e.g., the display device 107 and / or the management 114 shown in FIG. 1) perform the operation 400. By performing the operation 400, the analyte monitoring system determines a host’s disease progression based on changes in the host’s glycemic control levels over time. The analyte monitoring system may then determine and / or administer an appropriate treatment.

[0148] The analyte monitoring system begins by receiving analyte sensor measurements 402 from an analyte sensor system (e.g., the analyte sensor system 104 shown in FIG. 1). The analyte sensor measurements 402 may include values that indicate analyte levels (e.g., glucose levels) in a host. The analyte sensor measurements 402 may be received over time. In the example of FIG. 4, the analyte monitoring system receives the analyte sensor measurements402 A and 402B. The analyte sensor measurements 402B may be received after the analyte sensor measurements 402A, and the analyte sensor measurements 402B may indicate different analyte levels than the analyte sensor measurements 402A. The analyte monitoring system may receive additional analyte sensor measurements 402 over time.

[0149] In some embodiments, some of the analyte sensor measurements 402 may be generated based on certain actions or activities of the host. For example, some of the analyte sensor measurements 402 may be generated during or shortly after the host consumes a food or a meal. These analyte sensor measurements 402 may indicate increases in levels of certain analytes (e.g., glucose) as a result of the food or meal. As another example, some of the analyte sensor measurements 402 may be generated shortly after the host wakes in the morning. These analyte sensor measurements 402 may indicate rest or fasting level of certain analytes (e.g., glucose). As another example, some of the analyte sensor measurements 402 may be generated during different stages of pregnancy. These analyte sensor measurements 402 may indicate changes to certain levels of analytes as pregnancy progresses.

[0150] The analyte sensor measurements 402 may be generated using the same or different analyte sensor systems. For example, the analyte sensor measurements 402 may be generated across a large span of time in which the host replaces the analyte sensor system with another analyte sensor system. As a result, some of the analyte sensor measurements 402 may be generated by the same analyte sensor system or by a different analyte sensor system.

[0151] In some instances, the analyte monitoring system may discard analyte sensor measurements 402 that resulted from user error or that are artifacts. For example, if the analyte monitoring system determines that some analyte sensor measurements 402 are not realistic (e.g., exceed certain bounds) or do not fall within typical ranges of analyte levels for the host, the analyte monitoring system may discard the analyte sensor measurements 402 so that the analyte sensor measurements 402 are not further used or analyzed.

[0152] The analyte monitoring system analyzes the analyte sensor measurements 402 to determine glycemic control levels 404 of the host. For example, the analyte sensor measurements 402 may indicate glucose levels in the host after certain events (e.g., eating, awaking from sleep, different stages of pregnancy, etc.). The analyte monitoring system determines, from these glucose levels, the glycemic control level 404 of the host over time. For example, the analyte monitoring system may analyze analyte sensor measurements 402generated after the host eats a meal over a period of time. These analyte sensor measurements 402 may indicate increases in glucose levels. From these increases, the analyte monitoring system determines the host’s glycemic control levels 404 over time, which may indicate whether the host’s glycemic control levels 404 are improving or getting worse over time. As another example, the analyte monitoring system may analyze analyte sensor measurements 402 generated after the host awakes form sleep over a period of time. These analyte sensor measurements 402 may indicate resting or fasting glucose levels. Using this information, the analyte monitoring system determines the host’s glycemic control levels 404 over time, which may indicate whether the host’s glycemic control levels 404 are improving or getting worse over time. As another example, the analyte monitoring system may analyze analyte sensor measurements 402 generated during different stages of pregnancy. Using these analyte sensor measurements 402, the analyte monitoring system determines the host’s glycemic control levels 404 during the stages of pregnancy, and whether the glycemic control levels 404 are improving or getting worse.

[0153] In the example of FIG. 4, the analyte monitoring system determines a glycemic control level 404A from the analyte sensor measurements 402A and a glycemic control level 404B from the analyte sensor measurements 402B. The glycemic control level 404B may be determined after the glycemic control level 404A. As a result, the glycemic control levels 404A and 404B may indicate how the host’s glycemic control level changes over time or in response to certain events. The analyte monitoring system may determine the glycemic control levels 404 any number of times using any number of analyte sensor measurements 402.

[0154] The analyte monitoring system analyzes the glycemic control level 404 of the host over time to determine a disease progression 406 for the host. The disease progression 406 may indicate whether a disease (e.g., diabetes, gestational diabetes, etc.) is improving or getting worse. For example, if the glycemic control level 404 for the host is getting worse over time or crosses certain thresholds, the analyte monitoring system may determine that the host’s diabetes is getting worse. The analyte monitoring system may determine that the host is progressing from having a risk of diabetes to pre-diabetes or diabetes. As another example, the analyte monitoring system may determine that the host is developing gestational diabetes or that the gestational diabetes is getting worse.

[0155] The analyte monitoring system may determine and / or administer a treatment 408 for the disease progression 406. The treatment 408 may include pharmacological interventions and behavioral modifications. For example, if the disease progression 406 indicates that the host has diabetes or that the diabetes is becoming more severe, the analyte monitoring system may increase a dose of insulin and administer the increased dose of insulin (e.g., through a pump) to the host. As another example, if the disease progression 406 indicates that a risk of diabetes or gestational diabetes is increasing, the analyte monitoring system may instruct changes to diet and exercise to halt or slow down the disease progression 406.

[0156] FIG. 5 illustrates an example operation 500 for determining glycemic control levels performed by an analyte monitoring system (e.g., the analyte monitoring system 100 of FIG. 1). Generally, one or more components of the analyte monitoring system (e.g., the display device 107 and / or the management 114 shown in FIG. 1) perform the operation 500. By performing the operation 500, the analyte monitoring system determines a host’s glycemic control level based on analyte sensor measurements generated during or after the host consumes a meal.

[0157] The analyte monitoring system begins by receiving information about a meal 502 consumed by the host. For example, the information may include a time 504 of the meal 502. The information may also include the food consumed during the meal 502. This information may be included in a host database or a historical records database (e.g., the host database 110 or the historical records database 112 shown in FIG. 1).

[0158] The analyte monitoring system may also determine analyte sensor measurements 506 that were generated during or after the meal 502 was consumed. These analyte sensor measurements 506 may have been previously generated by an analyte sensor system (e.g., the analyte sensor system 104 shown in FIG. 1) and stored in the host database or historical records database. The analyte sensor measurements 506 may indicate analyte levels in the host, such as the host’s glucose levels. Other information may be stored with the analyte sensor measurements. For example, the analyte sensor measurements 506 may include a time 508 indicating when the analyte sensor measurements 506 were generated. The analyte monitoring system may use the time 508 and the time 504 to determine the analyte sensor measurements 506 that were generated during or shortly after the meal 502 was consumed.

[0159] The analyte monitoring system then analyzes the analyte sensor measurements 506 to determine a glycemic control level 510 of the host. For example, the analyte monitoring system may determine, from the food consumed during the meal 502, an expected increase in glucose levels for a healthy individual resulting from consuming the food. The analyte monitoring system may then compare the analyte sensor measurements 506 against the expected increase in glucose levels to determine the host’s glycemic control level 510. For example, if the analyte sensor measurements 506 reveal a higher glucose level than expected for a healthy individual, then the analyte monitoring system may determine a lower glycemic control level 510. If the analyte sensor measurements 506 reveal a lower glucose level than expected for a healthy individual, then the analyte monitoring system may determine a high glycemic control level 510. The analyte monitoring system may repeat the operation 500 across several meals to determine glycemic control levels 510 over time.

[0160] FIG. 6 illustrates an example operation 600 for determining glycemic control levels performed by an analyte monitoring system (e.g., the analyte monitoring system 100 of FIG. 1). Generally, one or more components of the analyte monitoring system (e.g., the display device 107 and / or the management 114 shown in FIG. 1) perform the operation 600. By performing the operation 600, the analyte monitoring system determines a host’s glycemic control level based on awakens fasting glucose level of the host.

[0161] The analyte monitoring system begins by receiving information about the host’s sleep patterns. For example, the information may include a wake time 602 indicating a time when the host woke up. The information may also include how long the host slept. This information may be included in a host database or a historical records database (e.g., the host database 110 or the historical records database 112 shown in FIG. 1).

[0162] The analyte monitoring system begins by receiving analyte sensor measurements 602 for the host. These analyte sensor measurements 602 may have been previously generated by an analyte sensor system (e.g., the analyte sensor system 104 shown in FIG. 1) and stored in the host database or historical records database. The analyte sensor measurements 602 may indicate analyte levels in the host, such as the host’s glucose levels. Other information may be stored with the analyte sensor measurements. For example, the analyte sensor measurements 602 may include a time 604 indicating when the analyte sensor measurements 602 were generated. The analyte monitoring system may cross reference the time 604 with mealconsumption information and / or sleep information for the host in the host database or historical records database to determine that the analyte sensor measurements 602 were generated after the host had not eaten for many hours. The analyte monitoring system may then determine a fasting glucose level 606 for the host using the analyte sensor measurements 602.

[0163] The analyte monitoring system then analyzes the fasting glucose level 606 to determine a glycemic control level 608 of the host. For example, the analyte monitoring system may determine an expected resting or fasting glucose level for a healthy individual. The analyte monitoring system may then compare the fasting glucose level 606 against the expected resting or fasting glucose level to determine the host’s glycemic control level 608. For example, if the fasting glucose level 606 is higher than the fasting glucose level expected for a healthy individual, then the analyte monitoring system may determine a lower glycemic control level 608. If the fasting glucose level 606 is lower than the fasting glucose level expected for a healthy individual, then the analyte monitoring system may determine a high glycemic control level 608. The analyte monitoring system may repeat the operation 600 to determine glycemic control levels 608 over time.

[0164] FIG. 7 illustrates an example operation 700 for determining disease progression performed by an analyte monitoring system (e.g., the analyte monitoring system 100 of FIG. 1). Generally, one or more components of the analyte monitoring system (e.g., the display device 107 and / or the management 114 shown in FIG. 1) perform the operation 700. By performing the operation 700, the analyte monitoring system determines a host’s disease progression based on the host’s glycemic control levels over time.

[0165] The analyte monitoring system begins with determined glycemic control levels 702 of the host. The glycemic control levels 702 may be determined at different times. By analyzing the glycemic control levels 702, the analyte monitoring system may determine how the glycemic control levels of the host change over time. For example, the analyte monitoring system may determine that the glycemic control levels of the host are improving or becoming worse. In the example of FIG. 7, the analyte monitoring system determines the glycemic control levels 702A and 702B. The glycemic control levels 702A and 702B may have been determined around similar events. For example, the glycemic control levels 702A and 702B may have been determined using analyte sensor measurements generated during or shortly after the host consumed a meal or during or shortly after the host woke up. As another example, theglycemic control levels 702 A and 702B may have been determined during different stages during pregnancy. The analyte monitoring system may determine and analyze any number of glycemic control levels 702 in the operation 700 to determine the disease progression of the host.

[0166] The analyte monitoring system also consider other information when determining the disease progression. For example, the analyte monitoring system may consider a disease type 704 of the host, a pregnancy status 706 of the host, and / or a due date 708 of the host. The disease type 704 may indicate a disease that the host has (e.g., Type I diabetes, Type II diabetes, gestational diabetes, etc.) or a stage of the disease that the host has (e.g., at risk of diabetes, pre-diabetes, diabetes). The pregnancy status 706 indicates whether the host is pregnant, and the due date 708 indicates when the host is expected to give birth if the host is pregnant. Using this information, the analyte monitoring system may set parameters (e.g., fixed and tunable parameters), rules, and thresholds (e.g., supported by clinical practice guidelines, medical literature, etc.) that allow the analyte monitoring system to determine a disease progression 710 of the host.

[0167] As an example, the analyte monitoring system may determine any number of metrics based on the glycemic control levels 702, such as an absolute amount of change of the glycemic control levels 702 of the host over a period of time, a rate of change of the glycemic control levels 702 of the host over a period of time, a second order rate of change (e.g., an acceleration or deceleration) of the glycemic control levels 702 of the host over a period of time, a value of the glycemic control levels 702, etc. The analyte monitoring system may compare one or more of these metrics against thresholds, and the analyte monitoring system may determine the disease progression 710 based on whether the metrics exceed the thresholds or based on for how long the metrics have exceeded the thresholds.

[0168] As an example, some of the glycemic metrics directly or indirectly derived from the analyte monitoring system include mean glucose, fasting glucose, GMI, pre-prandial glucose, post-prandial glucose, AUC (area under the curve), glycemic variability, peak glucose, number of hypoglycemic events, number of sustained hypoglycemic events, number of rebound hypoglycemic events, number of sustained hyperglycemic events, number of rebound hyperglycemic events, LBGI (low blood glucose index), HBGI (high blood glucose index), days of sensor wear, number of analyte data capture, and % glucose readings measured by theanalyte monitoring system within, above, or below prespecified or tunable therapeutic ranges (i.e. 70-180mg / dL for a senior with type 2 diabetes self-administering daily multiple daily insulin injections, 63-140mg / dL for a pregnant woman with gestational diabetes, 70-140mg / dL for a 35-year old adult with type 1 diabetes on an insulin delivery system integrated with an analyte monitoring system delivering automated insulin. These metrics may be computed over a short (i.e. 15min, 1-5 hours, daytime / nighttime, 24hrs, 3 days etc.) or long (i.e. 7 days, 14 days, 30days, 60 days, 90 days, 180days, 365 days, etc..) period of time and evaluated against thresholds individually or as weighted combination that is dependent on the disease type 704, disease progression 802, and other inputs 130. As an example, for a 26 year old adult who is a night shift worker, manages their type 1 diabetes with multiple daily injections of insulin delivered with an insulin injector device which automatically logs delivered doses and is integrated with the analyte monitoring system, has a BMI stable at 24, is normotensive, at low risk of cardiovascular disease, nephropathy, retinal disease, neuropathy, foot ulceration, infection, and depression, is at 11 years from type 1 diabetes onset, has strong self-management skills, and no neurocognitive or other impairments, some of their therapeutic glycemic thresholds within a 14 days revolving period may include a mean glucose values <154mg / dL, % fasting glucose between 70-125mg / dL target > 90%, GMI < 7%, % pre-prandial glucose between 80-130mg / dL > 10%, % peak glucose <180mg / dL > 75%, glycemic variability expressed as % CV < 36, number of hypoglycemic events between 0-4, number of hyperglycemic events 0-20, number of rebound hyperglycemic events <= 2, number of sustained hyperglycemic events 0-10, number of rebound hypoglycemic events between 0-2, an analyte data capture > 70% of expected values, % glucose readings measured by analyte monitoring system within 70-180mg / dL >70%, % glucose readings measured by analyte monitoring system within 54-69mg / dL <4%, % glucose readings measured by analyte monitoring system < 54mg / dL <1%, % glucose readings measured by analyte monitoring system within 181-250mg / dL <25%, % glucose readings measured by analyte monitoring system > 250mg / dL <5%.

[0169] In some embodiments, the analyte monitoring system uses artificial intelligence to determine the disease progression 710. For example, the analyte monitoring system may use a machine learning model (e.g., the trained model from the training server system 140 shown in FIG. 1) to analyze the glycemic control levels 702, the disease type 704, the pregnancystatus 706, and / or the due date 708 to determine the disease progression 710. The machine learning model may use neural networks, reinforcement learning, decision trees, etc.

[0170] The disease progression 710 may indicate a severity or state of the disease of the host. For example, the disease progression 710 may indicate whether the host is at risk of diabetes, has pre-diabetes, or has diabetes. As another example, the disease progression 710 may indicate whether the host is at risk of developing gestational diabetes or has gestational diabetes. The disease progression 710 may also indicate whether the disease is improving or getting worse.

[0171] As an example operation, the disease type 704 may indicate that the host is at risk for Type II diabetes. The analyte monitoring system may analyze the glycemic control levels 702 to determine whether the host is improving or getting worse at regulating glucose levels (e.g., after consuming a meal or while sleeping). If the glycemic control levels 702 improve over time, then the analyte monitoring system may determine that the host is improving at regulating glucose levels. The analyte monitoring system may determine a disease progression 710 that indicates that the host’s disease is improving. If the glycemic control levels 702 get worse over time, then the analyte monitoring system may determine that the host is getting worse at regulating glucose levels. The analyte monitoring system may determine a disease progression 710 that indicates that the host’ s disease is getting worse. For example, the analyte monitoring system may determine that the host has developed pre-diabetes or diabetes.

[0172] As another example operation, the disease type 704, pregnancy status 706, and / or due date 708 may indicate that the host is at risk of gestational diabetes. The analyte monitoring system may analyze the glycemic control levels 702 to determine whether the host is improving or getting worse at regulating glucose levels (e.g., after consuming a meal or while sleeping). If the glycemic control levels 702 improve over time, then the analyte monitoring system may determine that the host is improving at regulating glucose levels. The analyte monitoring system may determine a disease progression 710 that indicates that the host is no longer at risk of gestational diabetes or still at risk for gestational diabetes. If the glycemic control levels 702 get worse over time, then the analyte monitoring system may determine that the host is getting worse at regulating glucose levels, then the analyte monitoring system may determine a disease progression 710 that indicates that the host’s disease is getting worse. For example, the analyte monitoring system may determine that the host has developed gestational diabetes.

[0173] FIG. 8 illustrates an example operation 800 for determining and administering a treatment performed by an analyte monitoring system (e.g., the analyte monitoring system 100 of FIG. 1). Generally, one or more components of the analyte monitoring system (e.g., the display device 107 and / or the management 114 shown in FIG. 1) perform the operation 800. By performing the operation 800, the analyte monitoring system determines and / or administers a treatment for a disease progression of the host.

[0174] The analyte monitoring system may consider the disease progression 802 for the host along with metrics 804 and conditions 806 to determine a treatment 808. The disease progression 802 may indicate a disease type, disease stage, and / or disease severity for the host. For example, the disease progression 802 may indicate whether the host is developing or has diabetes (e.g., Type 1 or Type 2). As another example, the disease progression 802 may indicate whether the host is developing or has gestational diabetes. The metrics 804 may indicate aspects of the host’s behavior or activities. For example, the metrics 804 may indicate when the host ate a meal, a time when the host slept, a stress level of the host, a medication taken by the host, a glucose management indicator of the host, a weight of the host, a blood pressure of the host, non-glycemic sensor measurements of the host, or other clinical markers. The conditions 806 may indicate health or medical conditions of the host. For example, the conditions 806 may indicate a kidney disease of the host, obesity of the host, a cardiovascular disease of the host, a metabolic syndrome of the host, retinopathy of the host, neuropathy of the host, or an ulcer of the host. The metrics 804 and the conditions 806 may be stored in a host profile, a host database, or a historical records database (e.g., the host profile 118, the host database 110, or the historical records database 112 shown in FIG. 1). This information may provide the analyte monitoring system a full picture of the health status of the host when determining the treatment 808 for addressing the disease progression 802. For example, the analyte monitoring system may avoid treatment options that conflict with or worsen another condition 806 of the host. As another example, the analyte monitoring system may select treatment options that align with the metrics 804 of the host.

[0175] The treatment 808 may include any number of treatment options to address the disease progression 802. The analyte monitoring system may determine the treatment 808 using rules and thresholds set for the disease progression 802, the metrics 804, and / or the conditions 806. As an example, the rules and thresholds may include progression to diabeteswhen with a high degree of confidence 1) the host fasting glucose is >= 126mg / dL (>= 7.0mmol / L), or 2) the host A1C derived from analyte monitoring system measures is >= 6.5%, or 3) random glucose values derived from the analyte monitoring system is >=200mg / dL (> 11. Immol / L) accompanied by classic hyperglycemic symptoms such as polyuria, polydipsia, or unexplained weight loss, GMI or 2 hr postprandial glucose derived from the analyte monitoring system after oral ingestion of a 75-g glucose load of anhydrous glucose dissolved in water is > 200mg / dL (11. Immol / L), among other rules and thresholds. Another example of disease progression may include the detection of worsening glycemic status from a composite outcome of glycemic metrics including but not limited to those directly measured by the analyte monitoring system, which warrant a treatment intervention with a high degree of confidence.

[0176] In the example of FIG. 8, the treatment 808 includes a pharmacological intervention 810, a behavioral modification 812, and / or a health screening 814. The pharmacological intervention 810 may include medications or substances that the host should consume, along with dosages and times, to address the disease progression 802. The behavioral modification 812 may include changes to any behavior (e.g., diet, sleep, exercise, etc.). The health screening 814 may include any health screening (e.g., retinopathy, nephropathy, neuropathy, foot ulceration, peripheral arterial disease, etc.). The host may implement the behavioral modification 812 to address or improve the disease progression 802.

[0177] The analyte monitoring system may communicate the treatment 808 to any system or individual 816. For example, the analyte monitoring system may communicate the treatment 808 to the host and / or to a healthcare provider. In some instances, the treatment 808 may indicate the addition, removal, or dose change of oral or injectable pharmacotherapy agents. In this manner, the analyte monitoring system administers the treatment 808 by informing the host or healthcare provider about the treatment 808, which may reduce clinical inertia and improve the health and safety of the host. As another example, the analyte monitoring system may communicate the treatment 808 to a medical device (e.g., the medical device 208 shown in FIG. 2). In this manner, the analyte monitoring system administers the treatment by causing the medical device to administer the treatment. For example, the treatment 808 may indicate a dosage of insulin, and the analyte monitoring system may administer the dosage of insulin by causing an insulin pump to provide the dosage of insulin to the host. In some instances, theanalyte monitoring system may report the treatment 808 to assist in determining reimbursements and / or health insurance rates.

[0178] FIG. 9 is a flowchart of an example method 900 for determining and administering a treatment performed by an analyte monitoring system (e.g., the analyte monitoring system 100 of FIG. 1). By performing the method 900, the analyte monitoring system determines a disease progression of a host and administers treatment for the disease progression.

[0179] At 902, the analyte monitoring system generates first analyte sensor measurements. For example, the analyte monitoring system may include an analyte sensor system that measures analyte levels (e.g., glucose levels) in the host. The analyte sensor system may generate the first analyte sensor measurements, which indicate the analyte levels in the host. At 904, the analyte monitoring system generates second analyte sensor measurements. The second analyte sensor measurements may measure levels of the same analytes as the first analyte sensor measurements. The second analyte sensor measurements may be generated after the first analyte sensor measurements. The analyte monitoring system may use the same or a different analyte sensor system to generate the second analyte sensor measurements. For example, the first and second analyte sensor measurements may indicate analyte levels of the host during or shortly after the host consumed a meal. As another example, the first and second analyte sensor measurements may indicate analyte levels of the host during or shortly after the host woke up. As another example, the first and second analyte sensor measurements may indicate analyte levels of the host during different stages of pregnancy.

[0180] At 906, the analyte monitoring system determines a first glycemic control level of the host using the first analyte sensor measurements. At 908, the analyte monitoring system determines a second glycemic control level of the host using the second analyte sensor measurements. The glycemic control levels indicate how well the host regulates glucose levels. If the host regulates glucose levels well, then the glycemic control levels may be high. If the host does not regulate glucose levels well, then the glycemic control levels may be low. As an example, the glycemic control levels may be determined by composite outcomes of a variety of glycemic metrics (e.g. fasting glucose, average glucose, glycemic variability, time in range metrics, post-prandial glucose values, etc.) including but not limited to those directly measured by the analyte monitoring system, and other non-glycemic inputs which influence the degree of confidence when determining a disease progression of the host (e.g., the presence ofinfection, dehydration status, physical exertion, ingestion of certain phamacotherapy treatments such as steroids, etc. may lower the confidence of disease progression). The analyte monitoring system determines disease progression of the host by tracking glycemic control levels overtime.

[0181] At 910, the analyte monitoring system determines a disease progression of the host using the first and second glycemic control levels. Because the first and second glycemic control levels were generated using analyte sensor measurements from different times, the first and second glycemic control levels may indicate how the host’s glycemic control levels change over time. Using this information, the analyte monitoring system whether a disease of the host (e.g., diabetes, gestational diabetes) is improving or getting worse. For example, the analyte monitoring system may compare the glycemic control levels or metrics derived from the glycemic control levels against thresholds to determine the disease progression. The disease progression may indicate a type of disease that the host has (e.g., Type 1 or Type 2 diabetes, gestational diabetes, pre-diabetes, etc.) and a stage of that disease. The disease progression may also indicate whether the disease is improving or getting worse. For example, if the glycemic control levels of the host are improving, then the analyte monitoring system may determine a disease progression that indicates that the disease is improving or staying at the same stage. If the glycemic control levels are getting worse, then the analyte monitoring system may determine a disease progression that indicate that the disease is getting worse or progressing to later or subsequent stages.

[0182] The analyte monitoring system may also consider other factors when determining the disease progression of the host. For example, the analyte monitoring system may consider a disease type of the host (e.g., Type I diabetes, Type II diabetes, gestational diabetes, etc.), a pregnancy status of the host, and / or a due date of the host. For example, the analyte monitoring system may select different thresholds depending on one or more of these factors. The analyte monitoring system may then compare the glycemic control levels or the metrics derived from the glycemic control levels against the selected thresholds to determine the disease progression.

[0183] At 912, the analyte monitoring system administers a treatment for the disease progression. The analyte monitoring system may determine the treatment using rules and thresholds set for the disease progression. The treatment may include any type of intervention, such as pharmacological interventions and / or behavioral modifications. The analytemonitoring system may administer the treatment by communicating the treatment to the host and / or a healthcare provider. Additionally, the analyte monitoring system may administer the treatment by communicating the treatment to a medical device (e.g., an insulin pump) to provide a dosage of medicine or substance (e.g., insulin).

[0184] FIG. 10 is a block diagram depicting a computer system 1000, according to certain embodiments of the present disclosure. The computer system 100 may execute various components of the analyte monitoring system 100 shown in FIG. 1, such as the management engine 114 and / or the display device 107 shown in FIG. 1. Although depicted as a single physical device, in embodiments, the computer system 1000 may be implemented using virtual device(s), and / or across a number of devices, such as in a cloud environment. As illustrated, the computer system 1000 includes a processor 1005, a memory 1010, a storage 1015, a network interface 1025, and one or more input / output (I / O) interfaces 1020. In the illustrated embodiment, the processor 1005 retrieves and executes programming instructions stored in the memory 1010, as well as stores and retrieves application data residing in the storage 1015. The processor 1005 is generally representative of a single central processing unit (CPU) and / or graphics processing unit (GPU), multiple CPUs and / or GPUs, a single CPU and / or GPU having multiple processing cores, and the like. The memory 1010 is generally included to be representative of a random-access memory. The storage 1015 may be any combination of disk drives, flash-based storage devices, and the like, and may include fixed and / or removable storage devices, such as fixed disk drives, removable memory cards, caches, optical storage, network attached storage (NAS), or storage area networks (SAN).

[0185] In some embodiments, the input and output (I / O) devices 1035 (such as keyboards, monitors, etc.) can be connected via the I / O interface(s) 1020. Further, via network interface 1025, the computer system 1000 can be communicatively coupled with one or more other devices and components, such as the host database 110. In certain embodiments, the computer system 1000 is communicatively coupled with other devices via a network, which may include the Internet, local network(s), and the like. The network may include wired connections, wireless connections, or a combination of wired and wireless connections. As illustrated, the processor 1005, the memory 1010, the storage 1015, the network interface(s) 1025, and the I / O interface(s) 1020 are communicatively coupled by one or more interconnects 1030. In certain embodiments, the computer system 1000 is representative of a display device (e.g., the displaydevice 107 shown in FIG. 1) associated with the host. In certain embodiments, as discussed above, the display device can include the host’s laptop, computer, smartphone, and the like. In another embodiment, the computer system 1000 is a server executing in a cloud environment.

[0186] In the illustrated embodiment, the storage 1015 includes the host profile 118. The memory 1010 includes the management engine 114, which itself includes DAM 116.

[0187] According to an embodiment, an analyte monitoring system includes first sensor electronics, second sensor electronics, a memory, and a processor communicatively coupled to the memory. The first sensor electronics generate first analyte sensor measurements. The second sensor electronics generate second analyte sensor measurements. The processor receiver, from the first sensor electronics, the first analyte sensor measurements, determined, based on the first analyte sensor measurements, a first glycemic control level of a user, receives, from the second sensor electronics and after receiving the first analyte sensor measurements, the second analyte sensor measurements, determines, based on the second analyte sensor measurements, a second glycemic control level of the user, determines, based on the first glycemic control level of the user and the second glycemic control level of the user, a disease progression of the user, and administers a treatment for the disease progression.

[0188] The second sensor electronics may be the first sensor electronics.

[0189] Determining the first glycemic control level may be based on determining that the first analyte sensor measurements were generated within a time period after the user consumed a meal.

[0190] Determining the first glycemic control level may be based on determining, based on the first analyte sensor measurements, a fasting glucose level of the user.

[0191] Determining the disease progression may be based on the second glycemic control level being worse than the first glycemic control level.

[0192] The processor may receive a diabetes type and wherein determining the disease progression is based on the diabetes type.

[0193] The processor may receive a pregnancy status and wherein determining the disease progression is based on the pregnancy status.

[0194] The processor may determine the treatment based on at least one of a time when the user ate a meal, a time when the user slept, a stress level of the user, a medication taken by the user, a glucose management indicator of the user, a weight of the user, a blood pressure of the user, or non-glycemic sensor measurements of the user.

[0195] The processor may determine the treatment based on at least one of a kidney disease of the user, obesity of the user, a cardiovascular disease of the user, a metabolic syndrome of the user, retinopathy of the user, neuropathy of the user, or an ulcer of the user.

[0196] The treatment may include at least one of a pharmacological intervention or a modification of a behavior of the user.

[0197] According to another embodiment, a method includes generating, by first sensor electronics, first analyte sensor measurements, generating, by second sensor electronics, second analyte sensor measurements, receiving, from the first sensor electronics, the first analyte sensor measurements, determining, based on the first analyte sensor measurements, a first glycemic control level of a user, receiving, from the second sensor electronics and after receiving the first analyte sensor measurements, the second analyte sensor measurements, determining, based on the second analyte sensor measurements, a second glycemic control level of the user, determining, based on the first glycemic control level of the user and the second glycemic control level of the user, a disease progression of the user, and administering a treatment for the disease progression.

[0198] Determining the first glycemic control level may be based on determining that the first analyte sensor measurements were generated within a time period after the user consumed a meal.

[0199] Determining the first glycemic control level may be based on determining, based on the first analyte sensor measurements, a fasting glucose level of the user.

[0200] Determining the disease progression may be based on the second glycemic control level being worse than the first glycemic control level.

[0201] The method may include receiving a diabetes type and wherein determining the disease progression is based on the diabetes type.

[0202] The method may include receiving a pregnancy status and wherein determining the disease progression is based on the pregnancy status.

[0203] The method may include determining the treatment based on at least one of a time when the user ate a meal, a time when the user slept, a stress level of the user, a medication taken by the user, a glucose management indicator of the user, a weight of the user, a blood pressure of the user, or non-glycemic sensor measurements of the user.

[0204] The method may include determining the treatment based on at least one of a kidney disease of the user, obesity of the user, a cardiovascular disease of the user, a metabolic syndrome of the user, retinopathy of the user, neuropathy of the user, or an ulcer of the user.

[0205] The treatment may include at least one of a pharmacological intervention or a modification of a behavior of the user.

[0206] According to another embodiment, a non-transitory computer readable medium stores instructions that, when executed by a processor, cause the processor to receive first analyte sensor measurements from first sensor electronics, determine, based on the first analyte sensor measurements, a first glycemic control level of a user, receive, from second sensor electronics and after receiving the first analyte sensor measurements, second analyte sensor measurements, determine, based on the second analyte sensor measurements, a second glycemic control level of the user, determine, based on the first glycemic control level of the user and the second glycemic control level of the user, a disease progression of the user; and administer a treatment for the disease progression.

[0207] The phrases “analyte-measuring device,” “analyte-monitoring device,” “analytesensing device,” and / or “multi-analyte sensor device” as used herein are broad phrases, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to an apparatus and / or system responsible for the detection of, or transduction of a signal associated with, a particular analyte or combination of analytes. For example, these phrases may refer without limitation to an instrument responsible for detection of a particular analyte or combination of analytes. In one example, the instrument includes a sensor coupled to circuitry disposed within a housing, and configure to process signals associated with analyte concentrations into information. In one example, such apparatuses and / or systems are capable of providing specific quantitative, semi-quantitative, qualitative, and / or semi-qualitative analytical information using a biological recognition element combined with a transducing (detecting) element.

[0208] The terms “biosensor” and / or “sensor” as used herein are broad terms and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to a part of an analyte measuring device, analyte-monitoring device, analyte sensing device, and / or multianalyte sensor device responsible for the detection of, or transduction of a signal associated with, a particular analyte or combination of analytes. In one example, the biosensor or sensor generally comprises a body, a working electrode, a reference electrode, and / or a counter electrode coupled to body and forming surfaces configured to provide signals during electrochemical reactions. One or more membranes can be affixed to the body and cover electrochemically reactive surfaces. In one example, such biosensors and / or sensors are capable of providing specific quantitative, semi-quantitative, qualitative, semi qualitative analytical signals using a biological recognition element combined with a transducing (detecting) element.

[0209] The term “continuous” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an uninterrupted or unbroken portion, domain, coating, or layer.

[0210] The phrases “continuous analyte sensing” and “continuous multi-analyte sensing” as used herein are broad phrases, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refers without limitation to the period in which monitoring of analyte concentration is continuously, continually, and / or intermittently (but regularly) performed, for example, from about every second or less to about one week or more. In further examples, monitoring of analyte concentration is performed from about every 2, 3, 5, 7,10, 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 seconds to about every 1.25, 1.50, 1.75, 2.00, 2.25, 2.50, 2.75, 3.00, 3.25, 3.50, 3.75, 4.00, 4.25, 4.50, 4.75, 5.00, 5.25, 5.50, 5.75, 6.00, 6.25, 6.50, 6.75, 7.00, 7.25, 7.50, 7.75, 8.00, 8.25, 8.50, 8.75, 9.00, 9.25, 9.50 or 9.75 minutes. In further examples, monitoring of analyte concentration is performed from about 10, 20, 30, 40 or 50 minutes to about every 1, 2, 3, 4, 5, 6, 7 or 8 hours. In further examples, monitoring of analyte concentration is performed from about every 8 hours to about every 12, 16, 20, or 24 hours. In further examples, monitoring of analyte concentration is performed from about every day toabout every 1.5, 2, 3, 4, 5, 6, or 7 days. In further examples, monitoring of analyte concentration is performed from about every week to about every 1.5, 2, 3, or more weeks.

[0211] The term “coupled” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to two or more system elements or components that are configured to be at least one of electrically, mechanically, thermally, operably, chemically or otherwise attached. For example, an element is “coupled” if the element is covalently, communicatively, electrostatically, thermally connected, mechanically connected, magnetically connected, or ionically associated with, or physically entrapped, adsorbed to or absorbed by another element. Similarly, the phrases “operably connected”, “operably linked”, and “operably coupled” as used herein may refer to one or more components linked to another component(s) in a manner that facilitates transmission of at least one signal between the components. In some examples, components are part of the same structure and / or integral with one another as in covalently, electrostatically, mechanically, thermally, magnetically, ionically associated with, or physically entrapped, or absorbed (i.e., “directly coupled” as in no intervening element(s)). In other examples, 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; the signal can then be transmitted to an electronic circuit. In this example, the electrode is “operably linked” to the electronic circuit. The phrase “removably coupled” as used herein may refer to two or more system elements or components that are configured to be or have been electrically, mechanically, thermally, operably, chemically, or otherwise attached and detached without damaging any of the coupled elements or components. The phrase “permanently coupled” as used herein may refer to two or more system elements or components that are configured to be or have been electrically, mechanically, thermally, operably, chemically, or otherwise attached but cannot be uncoupled without damaging at least one of the coupled elements or components, covalently, electrostatically, ionically associated with, or physically entrapped, or absorbed.

[0212] The term “distal” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a region spaced relatively far from a point of reference, such as an origin or a point of attachment.

[0213] The term “ vivo” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and without limitation is inclusive of the portion of a device (for example, a sensor) adapted for insertion into and / or existence within a living body of a host.

[0214] The term “membrane” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a structure configured to perform functions including, but not limited to, protection of the exposed electrode surface from the biological environment, diffusion resistance (limitation) of the analyte, service as a matrix for a catalyst (e.g., one or more enzymes) for enabling an enzymatic reaction, limitation or blocking of interfering species, provision of hydrophilicity at the electrochemically reactive surfaces of the sensor interface, service as an interface between host tissue and the implantable device, modulation of host tissue response via drug (or other substance) release, and combinations thereof. When used herein, the terms “membrane” and “matrix” are meant to be interchangeable.

[0215] The term “proximal” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to the spatial relationship between various elements in comparison to a particular point of reference.

[0216] The term “sensitivity” as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an amount of signal (e.g., in the form of electrical current and / or voltage) produced by a predetermined amount (unit) of the measured analyte. For example, in one example, a sensor has a sensitivity (or slope) of about 1 to about 100 picoamps of current for every 1 mg / dL of analyte.

[0217] The methods disclosed herein comprise one or more steps or actions for achieving the methods. The method steps and / or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and / or use of specific steps and / or actions may be modified without departing from the scope of the claims.

[0218] As used herein, a phrase referring to “at least one of’ a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

[0219] The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language of the claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element is to be construed under the provisions of 35 U.S.C. § 112(f) unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.”

[0220] While various examples of the invention have been described above, it should be understood that they have been presented by way of example only, and not by way of limitation. Likewise, the various diagrams may depict an example architectural or other configuration for the disclosure, which is done to aid in understanding the features and functionality that can be included in the disclosure. The disclosure is not restricted to the illustrated example architectures or configurations, but can be implemented using a variety of alternative architectures and configurations. Additionally, although the disclosure is described above in terms of various example examples and aspects, it should be understood that the various features and functionality described in one or more of the individual examples are not limited in their applicability to the particular example with which they are described. They instead can be applied, alone or in some combination, to one or more of the other examples of thedisclosure, whether or not such examples are described, and whether or not such features are presented as being a part of a described example. Thus, the breadth and scope of the present disclosure should not be limited by any of the above-described examples.

[0221] All references cited herein are incorporated herein by reference in their entirety. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material.

[0222] Unless otherwise defined, all terms (including technical and scientific terms) are to be given their ordinary and customary meaning to a person of ordinary skill in the art and are not to be limited to a special or customized meaning unless expressly so defined herein.

[0223] Terms and phrases used in this application, and variations thereof, especially in the appended claims, unless otherwise expressly stated, should be construed as open-ended as opposed to limiting. As examples of the foregoing, the term ‘including’ should be read to mean ‘including, without limitation,’ ‘including but not limited to,’ or the like; the term ‘comprising’ as used herein is synonymous with ‘including,’ ‘containing,’ or ‘characterized by,’ and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps; the term ‘having’ should be interpreted as ‘having at least;’ the term ‘includes’ should be interpreted as ‘includes but is not limited to;’ the term ‘example’ is used to provide example instances of the item in discussion, not an exhaustive or limiting list thereof; adjectives

[0224] such as ‘known’, ‘normal’, ‘standard’, and terms of similar meaning should not be construed as limiting the item described to a given time period or to an item available as of a given time, but instead should be read to encompass known, normal, or standard technologies that may be available or known now or at any time in the future; and use of terms like ‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words of similar meaning should not be understood as implying that certain features are critical, essential, or even important to the structure or function of the invention, but instead as merely intended to highlight alternative or additional features that may or may not be utilized in a particular example of the invention. Likewise, a group of items linked with the conjunction ‘and’ should not be read as requiring that each and every one of those items be present in the grouping, but rather should be read as ‘and / or’ unless expressly stated otherwise. Similarly, a group of items linked with the conjunction ‘or’ should not be read as requiring mutual exclusivity among that group, but rather should be read as ‘and / or’ unless expressly stated otherwise.

[0225] The term “comprising as used herein is synonymous with “including.” “containing,” or “characterized by” and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.

[0226] All numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification are to be understood as being modified in all instances by the term ‘about.’ Accordingly, unless indicated to the contrary, the numerical parameters set forth herein are approximations that may vary depending upon the desired properties sought to be obtained. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of any claims in any application claiming priority to the present application, each numerical parameter should be construed in light of the number of significant digits and ordinary rounding approaches.

[0227] Furthermore, although the foregoing has been described in some detail by way of illustrations and examples for purposes of clarity and understanding, it is apparent to those skilled in the art that certain changes and modifications may be practiced. Therefore, the description and examples should not be construed as limiting the scope of the invention to the specific examples and examples described herein, but rather to also cover all modification and alternatives coming with the true scope and spirit of the invention.

Claims

CLAIMS1. An analyte monitoring system comprising: first sensor electronics configured to generate first analyte sensor measurements; second sensor electronics configured to generate second analyte sensor measurements; a memory; and a processor communicatively coupled to the memory, the processor configured to: receive, from the first sensor electronics, the first analyte sensor measurements; determine, based on the first analyte sensor measurements, a first glycemic control level of a user; receive, from the second sensor electronics and after receiving the first analyte sensor measurements, the second analyte sensor measurements; determine, based on the second analyte sensor measurements, a second glycemic control level of the user; determine, based on the first glycemic control level of the user and the second glycemic control level of the user, a disease progression of the user; and administer a treatment for the disease progression.

2. The analyte monitoring system of Claim 1, wherein the second sensor electronics is the first sensor electronics.

3. The analyte monitoring system of Claim 1, wherein determining the first glycemic control level is based on determining that the first analyte sensor measurements were generated within a time period after the user consumed a meal.

4. The analyte monitoring system of Claim 1, wherein determining the first glycemic control level is based on determining, based on the first analyte sensor measurements, a fasting glucose level of the user.

5. The analyte monitoring system of Claim 1, wherein determining the disease progression is based on the second glycemic control level being worse than the first glycemic control level.

6. The analyte monitoring system of Claim 1, wherein the processor is configured to receive a diabetes type and wherein determining the disease progression is based on the diabetes type.

7. The analyte monitoring system of Claim 1, wherein the processor is configured to receive a pregnancy status and wherein determining the disease progression is based on the pregnancy status.

8. The analyte monitoring system of Claim 1, wherein the processor is configured to determine the treatment based on at least one of a time when the user ate a meal, a time when the user slept, a stress level of the user, a medication taken by the user, a glucose management indicator of the user, a weight of the user, a blood pressure of the user, or non-glycemic sensor measurements of the user.

9. A method comprising: generating, by first sensor electronics, first analyte sensor measurements; generating, by second sensor electronics, second analyte sensor measurements; receiving, from the first sensor electronics, the first analyte sensor measurements; determining, based on the first analyte sensor measurements, a first glycemic control level of a user; receiving, from the second sensor electronics and after receiving the first analyte sensor measurements, the second analyte sensor measurements; determining, based on the second analyte sensor measurements, a second glycemic control level of the user; determining, based on the first glycemic control level of the user and the second glycemic control level of the user, a disease progression of the user; and administering a treatment for the disease progression.

10. The method of Claim 9, wherein determining the first glycemic control level is based on determining that the first analyte sensor measurements were generated within a time period after the user consumed a meal.

11. The method of Claim 9, wherein determining the first glycemic control level is based on determining, based on the first analyte sensor measurements, a fasting glucose level of the user.

12. The method of Claim 9, wherein determining the disease progression is based on the second glycemic control level being worse than the first glycemic control level.

13. The method of Claim 9, further comprising receiving a diabetes type and wherein determining the disease progression is based on the diabetes type.

14. The method of Claim 9, further comprising receiving a pregnancy status and wherein determining the disease progression is based on the pregnancy status.

15. A non-transitory computer readable medium storing instructions that, when executed by a processor, cause the processor to: receive first analyte sensor measurements from first sensor electronics; determine, based on the first analyte sensor measurements, a first glycemic control level of a user; receive, from second sensor electronics and after receiving the first analyte sensor measurements, second analyte sensor measurements; determine, based on the second analyte sensor measurements, a second glycemic control level of the user; determine, based on the first glycemic control level of the user and the second glycemic control level of the user, a disease progression of the user; and administer a treatment for the disease progression.

Images