Medicament utilization based therapy optimization

Abstract

In some embodiments, a method of automatically adjusting insulin therapy for a patient includes receiving, at a device associated with the patient, one or more glucose measurements generated by a continuous glucose monitoring (CGM) system of the patient for a period. The method also includes determining, at the device, basal insulin data and bolus insulin data of the patient for the period. The method also includes determining, at the device, a relationship between the basal insulin data and the bolus insulin data. The method also includes generating, at the device, an insulin therapy instruction for the patient based on the one or more glucose measurements and the determined relationship between the basal insulin data and the bolus insulin data, where the insulin therapy instruction includes a total bolus amount.

Description

MEDICAMENT UTILIZATION BASED THERAPY OPTTMTZATTONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and benefit of U.S. Provisional Patent Application No. 63 / 739,416 filed December 27, 2024, which application is hereby expressly incorporated by reference herein in its entirety as if fully set forth below and for all applicable purposes.INTRODUCTION

[0002] Diabetes is a metabolic condition affecting hundreds of millions of people. For these people, monitoring blood glucose levels is important not only to mitigate long-term issues such as heart disease and vision loss, but also to avoid the effects of hyperglycemia and hypoglycemia.SUMMARY

[0003] In some embodiments, one general aspect includes a system for automatically adjusting insulin therapy for a patient. The system includes one or more memories having executable instructions. The system also includes one or more processors in data communication with the one or more memories and configured to execute the executable instructions to execute a process for automatically adjusting insulin therapy for the patient. The process includes receiving glucose data from a continuous glucose monitoring (CGM) sensor indicative of a glucose level of the patient over a time period. The process also includes determining basal insulin data and bolus insulin data of the patient for the time period. The process also includes determining a relationship between the basal insulin data and the bolus insulin data. The process also includes generating an insulin therapy instruction for the patient based on the glucose data and the determined relationship between the basal insulin data and the bolus insulin data, where the insulin therapy instruction includes a total bolus amount.

[0004] In some embodiments, another general aspect includes a method of automatically adjusting insulin therapy for a patient. The method includes receiving, at a device associated with the patient, one or more glucose measurements generated by a continuous glucose monitoring (CGM) sensor of the patient for a time period. The method also includes determining, at the device, basal insulin data and bolus insulin data of the patient for the time period. The method also includes determining, at the device, a relationship between the basal insulin data and the bolus insulin data.The method also includes generating, at the device, an insulin therapy instruction for the patient based on the one or more glucose measurements and the determined relationship between the basal insulin data and the bolus insulin data, where the insulin therapy instruction includes a total bolus amount.BRIEF DESCRIPTION OF THE DRAWINGS

[0005] FIG. 1A illustrates an example of a therapy management system, in accordance with certain embodiments.

[0006] FIG. IB illustrates an example analyte sensor system including an example continuous analyte sensor(s) with sensor electronics, in accordance with certain embodiments.

[0007] FIG. 2 illustrates example inputs and example outputs that are generated based on the inputs, in accordance with certain embodiments.

[0008] FIG. 3 illustrates an example of a process for generating insulin therapy settings, in accordance with certain embodiments.

[0009] FIG. 4 illustrates an example of a process for executing real-time insulin therapy based on insulin therapy settings, in accordance with certain embodiments.

[0010] FIG. 5 illustrates an example of a process for generating an insulin therapy instruction, in accordance with certain embodiments.

[0011] FIG. 6 is a block diagram depicting a computer system configured for adjusting insulin therapy based on current insulin utilization, in accordance with certain embodiments.DETAILED DESCRIPTION

[0012] Advances in medical technologies have enabled development of various systems for monitoring blood glucose, including continuous glucose monitoring (CGM) systems, which measure and record glucose concentrations in substantially real-time. CGM systems are important tools for users of these systems to ensure that measured glucose values are within an acceptable target range (e.g., 70-180 mg / dL). For people with diabetes, monitoring blood glucose levels and regulating those levels to be within an acceptable target range is important not only to mitigate long-term issues such as heart disease and vision loss, but also to avoid the effects of hyperglycemia and hypoglycemia.

[0013] Maintaining blood glucose levels within an acceptable target range can be challenging. Glucose levels fluctuate naturally throughout the day and also in response to certain behaviors such as meal intake or exercise. In an effort to control glucose levels, conventional insulin therapy may involve a combination of basal and bolus administration. Basal administration is typically used to regulate glucose levels by providing a baseline level of insulin throughout a 24-hour day (e.g., between meals and while sleeping), for example, according to a basal rate. The basal rate can be, for example, a pre-set pattern that varies throughout the day. Bolus administration is typically used to regulate glucose levels in anticipation of, and / or in response to, behaviors that increase glucose levels, such as meals, as well as to correct elevated glucose levels not adequately addressed by basal administration.

[0014] In certain aspects, basal and / or bolus administration can be implemented via an insulin pump and / or injections. In an example, in implementations utilizing an insulin pump, basal administration can be accomplished via scheduled insulin deliveries according to a basal rate, while bolus administration can be accomplished via other deliveries of precise amounts of insulin, for example, in any of the bolus scenarios discussed previously. In another example, in an implementation utilizing injections, basal administration can be accomplished via periodic injection of a first insulin (e.g., long-acting insulin once per day), while bolus administration can be implemented via injections of another, faster-acting insulin, as needed. Other examples of basal and / or bolus administration (e.g., oral methods) will be apparent to one skilled in the art after a detailed review of the present disclosure.

[0015] According to conventional insulin therapy, a patient’s insulin may be split between basal and bolus insulin administration in a standardized way (e.g., a 50 / 50 split). Even with diligent monitoring using a CGM system, it is common for a patient’s glucose levels to be outside of an acceptable target range (e.g., 70-180 mg / dL) a third or more of the time. For example, a basal rate is often not sufficient, by itself, to manage the natural fluctuation in glucose levels throughout the day. In such cases, corrective action may be taken, for example, in the form of an insulin bolus. However, such corrective action may occur only after glucose levels have already exceeded an upper boundary of a target range, such as 180, thus ensuring additional time outside the target range as the corrective action takes effect. Furthermore, while more time in range is generally desirable, and hyperglycemia is generally undesirable, aggressive insulin therapy to correct or prevent hyperglycemia may create a severe risk of hypoglycemia. Therefore, conventional bolusesto reactively correct hyperglycemia may be conservatively calculated to slowly transition the patient’s glucose levels back to the target range. While such methods minimize a risk of hypoglycemia, they can also prolong the amount of time spent in hyperglycemia and, hence, decrease a time in range.

[0016] According to conventional insulin therapy, insulin boluses (e.g., patient-initiated boluses) may also be administered to account for certain behavioral events, such as meals. Bolus amounts are typically determined based on carbohydrate ingestion, for example, using insulin-to-carbohydrate ratios. Such determination methods, however, are often inaccurate due to the difficulty in accurately counting carbohydrates and also due to other meal-related factors that may directly or indirectly impact glucose levels, such as meal timing, fat content, recent exercise, etc.

[0017] In response to the above problems, in certain aspects described herein, a therapy management system may be provided that establishes and / or periodically adjusts a patient’ s insulin therapy settings. The insulin therapy settings can include, for example, a target ratio between basal insulin delivery and bolus insulin delivery, sometimes referred to herein as a target basal / bolus ratio. The insulin therapy settings can further include, for example, a basal rate for the patient. In certain aspects, the target basal / bolus ratio and the basal rate, for example, can be established and / or periodically adjusted based on the patient’s glucose control (e.g., time in range) and a relationship between their basal insulin delivery and their bolus insulin delivery (e.g., a historical basal / bolus ratio).

[0018] For example, in certain aspects, the therapy management system can variably weight the patient’s basal, bolus, and total insulin over different periods of time, such as one day, two days, 6 days, 60 days and 90 days (e.g., giving more weight to more recent data). The therapy management system can set or incrementally adjust a basal rate and a target basal / bolus ratio towards an aspirational basal / bolus ratio, such as 35 / 65, 25 / 75, 0 / 100, or another suitable ratio. In some aspects, the therapy management engine can incrementally adjust the basal rate and the target basal / bolus ratio until the patient’s time in range achieves a configurable target (e.g., 95 %). Advantageously, in certain aspects, by putting more weight on insulin utilization closer to a present time, the therapy management system can emphasize more recent insulin utilization (e.g., historical basal / bolus ratio and total insulin), while still factoring in insulin utilization over longerperiods of time to account for short-term variation (e.g., different behaviors due to the patient being on vacation).

[0019] In further response to the above problems, in certain aspects described herein, the therapy management system can execute real-time insulin therapy adjustments based on the insulin therapy settings. For example, the therapy management engine can make real-time decisions on small boluses (e.g., one unit, half unit, etc.) based on how the patient’s current insulin utilization compares to the patient’s weighted historical insulin utilization. In certain aspects, the therapy management system can execute a process for making such real-time decisions on a configurable interval such as every 20 minutes, every 30 minutes, every hour, etc. In some aspects, the therapy management system can execute the process for making real-time decisions at least hourly.

[0020] In certain aspects, the execution of a process for making real-time decisions on a configurable interval can result in smaller, more frequent boluses as compared conventional insulin therapy that uses, for example, a 50 / 50 split between basal and bolus insulin administration. In certain aspects, such smaller, more frequent boluses can improve patient health. In an example, if a patient is subject to five 4-unit boluses in a given day under conventional insulin therapy, according to various approaches described herein, the same patient can instead be subject to twenty one-unit boluses over the same time period. Advantageously, in certain aspects, more frequent, smaller bolus doses can cause a more rapid reaction to rising glucose levels, thereby improving time in range, lowering an average glucose, reducing a risk and frequency of hyperglycemia, and improving the patient’s A1C. Further, in certain aspects, more frequent, smaller bolus doses can reduce an amount and severity of overcorrections that cause, for example, extra glucose intake to correct or prevent hypoglycemia.

[0021] While more frequent, smaller bolus doses generally improve overall patient health as discussed above, in some cases, there is a mental burden associated with having to implement numerous decisions throughout the day. In certain aspects, the therapy management engine can automatically implement the smaller, more frequent boluses discussed above, thereby avoiding the imposition of additional treatment decisions on the patient and the concomitant risk of decision fatigue. For example, bolus amounts can be determined and / or administered, as applicable, every 20 minutes, every 30 minutes, every hour, and / or the like, thereby reducing the patient’s mental burden. Furthermore, the approaches described herein can reduce or eliminate reliance oncarbohydrate counting, which can further reduce the patient’s mental burden as well as reduce or eliminate a need for manual meal entry and / or a need for external data sources for carbohydrate data.

[0022] The techniques described herein for adjusting insulin therapy based on current insulin utilization are described more fully herein with respect to FIGS. 1A-B and 2-6 below. Note that although certain aspects herein are periodically described with respect to the management of diabetes and insulin treatment, the techniques described herein are similarly applicable to other diseases or conditions (e.g., other autoimmune diseases) and hormones other than insulin. For example, in various aspects, techniques described herein can be applied to amylin, glucagon, leptin, and / or other hormones not adequately produced by a given patient, for example, due to an autoimmune disease.

[0023] As used herein, the term “continuous” analyte monitoring refers to monitoring one or more analytes in a fully continuous, semi -continuous, periodic manner, which results in a data stream of analyte values over time. A data stream of analyte values over time is what allows for meaningful data and insights to be derived using the algorithms described herein for adjusting insulin therapy based on current insulin utilization. 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 any meaningful data or insight to be derived. As such, without the continuous analyte monitoring system of the embodiments herein, it is simply impossible to continuously adjust insulin therapy based on current insulin utilization, as described herein.

[0024] 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. The derived real-time data and insight in turn allows for adjusting insulin therapy based on current insulin utilization. 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 current(s) (i.e., analog electrical signals) generated by the continuous analyte sensor(s) into sensor count values, (2) calibrating the count values to generate at least glucose and / or other analyteconcentration values using calibration techniques described herein to account for the sensitivity of the continuous analyte sensor(s), and (3) transmitting measured glucose and / or other analyte concentration data, including glucose and / or other analyte concentration values, to a display device via wireless connection.

[0025] For example, the at least one sensor electronics module may be configured to 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 to transmit the measured glucose and / or other 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.

[0026] The real-time analyte data that is continuously generated by the continuous analyte monitoring system described herein, therefore, allows the therapy management system herein to adjust insulin therapy based on current insulin utilization, in real-time, which is technically impossible to perform using existing or conventional techniques or systems. Further, because of the real-time nature of this data, it is also 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 to adjust insulin therapy based on current insulin utilization. In other words, deriving meaningful data and insight from a stream of real-time 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 algorithm described in relation to FIGS. 4-5, in real-time and on a continuous basis, which would involve using a stream of real-time data that is continuously generated by a patient’s continuous analyte monitoring system and / or significantly large amount of population data (e.g., hundreds or thousands of data points for each one of thousands or millions of patients in the patient population) is not a task that can be mentally performed, especially in real-time at times.

[0027] Further, certain embodiments herein are directed to a technical solution to a technical problem associated with analyte sensor systems. In particular, each analyte sensor system that is manufactured by a sensor manufacturer might perform slightly differently. As such, there might be inconsistencies between sensors and the measurements they generate once in use. Accordingly, certain embodiments herein are directed to determining the performance of an analyte sensorsystem during a manufacturing calibration process (in vitro), which includes quantifying certain sensor operating parameters, such as a calibration slope (also known as calibration sensitivity), a calibration baseline, etc.

[0028] Generally, calibration sensitivity refers to the amount of electrical current produced by an analyte sensor of an analyte sensor system when immersed in a predetermined amount of a measured analyte. The amount of electrical current may be expressed in units of picoAmps (pA) or counts. The amount of measured analyte may be expressed as a concentration level in units of milligrams per deciliter (mg / dL), and the calibration sensitivity may be expressed in units of pA / (mg / dL) or counts / (mg / dL). The calibration baseline refers to the amount of electrical current produced by the analyte sensor when no analyte is detected, and may be expressed in units of pA or counts.

[0029] The calibration sensitivity, calibration baseline, and other information related to the sensitivity profde for the analyte sensor system may be programmed into the sensor electronics module of the analyte sensor system during the manufacturing process, and then used to convert the analyte sensor electrical signals into measured analyte concentration levels. For example, the calibration slope (calibration sensitivity) may be used to predict an initial in vivo sensitivity (Mo) and a final in vivo sensitivity (Mf), which are programmed into the sensor electronics module and used to convert the analyte sensor electrical signals into measured analyte concentration levels.

[0030] In certain embodiments, during in vivo use, the sensor electronics module of an analyte sensor system samples the analog electrical signals produced by the analyte sensor to generate analyte sensor count values, and then determines the measured analyte concentration levels based on the analyte sensor count values, the initial in vivo sensitivity (Mo), and the final in vivo sensitivity (Mf). For example, measured analyte concentration levels may be determined using a sensitivity function M(t) that is based on the initial in vivo sensitivity (Mo) and the final in vivo sensitivity (Mf). The sensitivity function M(t) may expressed in several different ways, such as a simple correction factor that is not dependent on elapsed time (ti) of in vivo use, a linear relationship between sensitivity and time (ti), an exponential relationship between sensitivity and time (ti), etc. Equation 1 presents one technique for determining a measured analyte concentration level (ACL) from an analyte sensor count value (count) at a time ti:ACL = count / M(ti) Eq. 1A calibration baseline (baseline) may also be used to determine a measured analyte concentration level (ACL) from an analyte sensor count value (count) at a time ti, and Equation 2 presents one technique:ACL = (count - baseline) / M(ti) Eq. 2

[0031] FIG. 1A illustrates an example of a therapy management system 100 for adjusting insulin therapy based on current insulin utilization, in accordance with certain embodiments of the disclosure. The therapy management system 100 may be utilized for generating and presenting information related to user health, for example, using various user interfaces associated with system 100. Each user of system 100, such as user 102, may interact with a mobile health application, such as mobile health application (“application”) 106 (e.g., a diabetes intervention application that provides therapy management guidance), and / or a health monitoring device, such as an analyte sensor system 104 (e.g., a glucose monitoring system). User 102, in certain embodiments, may be the patient or, in some cases, the patient’s caregiver. In the embodiments described herein, the user is assumed to be the patient for simplicity only, but is not so limited. As shown, system 100 may include an analyte sensor system 104, a display device 107 that executes application 106, a therapy management engine 112, and a user database 110.

[0032] Analyte sensor system 104 may be configured to generate time-series data, such as analyte measurements (e.g., sensor data), for the user 102, e.g., on a continuous basis, and transmit the analyte measurements to the display device 107 for use by application 106. In some embodiments, the analyte sensor system 104 may transmit the analyte measurements to the display device 107 through a wireless connection (e.g., Bluetooth connection). In certain embodiments, display device 107 is a smart phone. However, in certain embodiments, display device 107 may instead be any other type of computing device such as a laptop computer, a smartwatch, a tablet, or any other computing device capable of executing application 106.

[0033] Note that, while in certain examples the analyte sensor system 104 is assumed to be a glucose monitoring system, 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).

[0034] 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.

[0035] 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; carnosinase; CD4; ceruloplasmin; chenodeoxy cholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-0 hydroxy-cholic 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, betathalassemia, 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, ); lysozyme; mefloquine; netilmicin; phenobarbitone; phenyloin; phytanic / pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse triiodothyronine (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.

[0036] 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-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5 -hydroxy tryptamine (5HT), histamine, Advanced Glycation End Products (AGEs) and 5-hydroxyindoleacetic acid (FHIAA).

[0037] Application 106 may be a mobile health application that is configured to receive and analyze time-series data, including analyte measurements, from the analyte sensor system 104 and / or other devices, as described in greater detail relative to FIGS. IB and 2. In some embodiments, application 106 may transmit analyte measurements received from the analyte sensor system 104 to a user database 110 (and / or the therapy management engine 112), and the user database 110 (and / or the therapy management engine 112) may store the analyte measurements in a user profile 118 of user 102 for processing and analysis, for example, by the therapy management engine 112. In some embodiments, application 106 may store the analyte measurements in a user profile 118 of user 102 locally for processing and analysis, for example, by the therapy management engine 112.

[0038] In certain embodiments, therapy management engine 112 refers to a set of software instructions with one or more software modules, including a data analysis module (DAM) 111. In some embodiments, therapy management engine 112 executes entirely on one or more computing devices in a private or a public cloud. In some other embodiments, therapy management engine 112 executes partially on one or more local devices, such as display device 107 (e.g., via application 106) and / or analyte sensor system 104, and partially on one or more computing devices in a private or a public cloud. In some other embodiments, therapy management engine 112 executes entirely on one or more local devices, such as display device 107 (e.g., via application 106) and / or analyte sensor system 104.

[0039] In certain embodiments, DAM 111 of therapy management engine 112 may be configured to receive and / or process a set of inputs 127 (described in more detail below) (also referred to herein as “input data”) to determine one or more outputs 130 (also referred to herein as “metrics data”). Inputs 127 may be stored in the user profile 118 in the user database 110. DAM 111 can fetch inputs 127 from the user database 110 and compute a plurality of outputs 130 whichcan then be stored as application data 126 in the user profile 118. Such outputs 130 may include health-related metrics.

[0040] In certain embodiments, application 106 is configured to take as input information relating to user 102 and store the information in a user profile 118 for user 102 in user database 110. For example, application 106 may obtain and record user 102’s demographic info 119, disease progression info 121, and / or medication info 122 in user profile 118. In certain embodiments, demographic info 119 may include one or more of the user’s age, body mass index (BM1), ethnicity, gender, etc. In certain embodiments, disease progression info 121 may include information about the user 102’s disease, such as, for diabetes, whether the user is Type I, Type II, pre-diabetes, or whether the user has gestational diabetes. In certain embodiments, disease progression info 121 also includes the length of time since diagnosis, the level of disease control, level of compliance with disease management therapy, predicted pancreatic function, other types of diagnosis (e.g., heart disease, obesity) or measures of health (e.g., heart rate, exercise, stress, sleep, etc ), and / or the like. In certain embodiments, medication info 122 may include information about the amount and type of medication taken by user 102, such as insulin or non-insulin diabetes medications and / or non-diabetes medication taken by user 102.

[0041] In certain embodiments, application 106 may obtain demographic info 119, disease progression info 121, and / or medication info 122 from the user 102 in the form of user input or from other sources. In certain embodiments, as some of this information changes, application 106 may receive updates from the user 102 or from other sources. In certain embodiments, user profile 118 associated with the user 102, as well as other user profiles associated with other users are stored in a user database 110, which is accessible to application 106, as well as to the therapy management engine 112, over one or more networks (not shown).

[0042] In certain embodiments, application 106 collects inputs 127 through user 102 input and / or a plurality of other sources, including analyte sensor system 104, other applications running on display device 107, and / or one or more other sensors and devices. In certain embodiments, such sensors and devices include one or more of, but are not limited to, an insulin pump, other types of analyte sensors, sensors or devices provided by display device 107 (e.g., accelerometer, camera, global positioning system (GPS), heart rate monitor, etc.) or other user accessories (e.g., a smartwatch), or any other sensors or devices that provide relevant information about the user 102.In certain embodiments, user profile 118 also stores application configuration information indicating the current configuration of application 106, including its features and settings.

[0043] User database 110, in some embodiments, refers to a storage server that may operate in a public or private cloud. User 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 exemplary implementations, user database 110 is distributed. For example, user database 110 may comprise a plurality of persistent storage devices, which are distributed. Furthermore, user database 110 may be replicated so that the storage devices are geographically dispersed.

[0044] User database 110 may include other user profiles 118 associated with a plurality of other users served by therapy management system 100. More particularly, 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 102. For such users, user profiles 118 are similarly created and stored in user database 110.

[0045] FIG. IB is a diagram 150 conceptually illustrating an example continuous analyte sensor system 104 including example continuous analyte sensor(s) with sensor electronics, in accordance with certain aspects of the present disclosure. For example, system 104 may be configured to continuously monitor one or more analytes of a patient, in accordance with certain aspects of the present disclosure.

[0046] Continuous analyte sensor system 104 in the illustrated embodiment includes sensor electronics module 138 and one or more continuous analyte sensor(s) 140 (individually referred to herein as continuous analyte sensor 140 and collectively referred to herein as continuous analyte sensors 140) associated with sensor electronics module 138. Sensor electronics module 138 may be in wireless communication (e.g., directly or indirectly) with one or more of display devices 107a, 107b, 107c, and 107d. In certain embodiments, sensor electronics module 138 may also be in wireless communication (e.g., directly or indirectly) with one or more medical devices, such as medical devices 108 (individually referred to herein as medical device 108 and collectively referred to herein as medical devices 108), and / or one or more other non-analyte sensors 142(individually referred to herein as non-analyte sensor 142 and collectively referred to herein as non-analyte sensor 142).

[0047] In certain embodiments, a continuous analyte sensor 140 may comprise one or more sensors for detecting and / or measuring analyte(s). The continuous analyte sensor 140 may be a multi-analyte sensor configured to continuously measure two or more analytes or a single analyte sensor configured to continuously measure a single analyte as a non-invasive device, a subcutaneous device, a transcutaneous device, a transdermal device, and / or an intravascular device. In certain embodiments, the continuous analyte sensor 140 may be configured to continuously measure analyte levels of a patient 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 140 provides a data stream indicative of the concentration of one or more analytes in the patient. 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 patient.

[0048] In certain embodiments, the continuous analyte sensor 140 may be a multi-analyte sensor, configured to continuously measure multiple analytes in a patient’s body. For example, in certain embodiments, the continuous multi-analyte sensor 140 may be a single sensor configured to measure lactate, glucose, ketones (e.g., 3-beta-hydroxybutyrate, acetoacetate, acetone, etc.), glycerol, and / or free fatty acids in the patient’s body.

[0049] In certain embodiments, one or more multi -analyte sensors may be used in combination with one or more single analyte sensors. As an illustrative example, a multi-analyte sensor may be configured to continuously measure lactate and glucose and may, in some cases, be used in combination with an analyte sensor configured to measure only ketones or only potassium. 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. In further embodiments, 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 scaleinformation or non-contact heart rate monitoring from a sensor pad under the patient while in a chair or bed, through an infra-red camera detecting temperature and / or blood flow patterns of the patient, and / or through a visual camera with machine vision for height, weight, or other parameter estimation without physical contact.

[0050] In certain embodiments, the continuous analyte sensor(s) 140 may comprise a percutaneous wire that has a proximal portion coupled to the sensor electronics module 138 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 the 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 138. After the continuous analyte monitoring system 104 has been applied to epidermis of the patient, continuous analyte sensor(s) 140 penetrates the epidermis, and the distal portion extends into the dermis and / or subcutaneous tissue under epidermis. Other configurations of continuous analyte sensor(s) 140 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.

[0051] 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, continuous analyte sensor 140 may include a single-analyte sensor configured to measure lactate concentration levels, and another single-analyte sensor configured to measure glucose concentration levels of the patient. As another illustrative example, continuous analyte sensor(s) 140 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, continuous analyte sensor(s) 140 may include a multi-analyte sensor configured to measure lactate concentration levels, glucose concentration levels, ketone concentration levels, creatinine concentration levels, etc. Accordingly, continuous analyte sensor(s) 140 is configured to generate at least one analogelectrical signal that is proportional to the concentration level of a particular analyte, and sensor electronics module 138 is configured to convert the analog electrical signal into an analyte sensor count values, calibrate the analyte sensor count values based on the sensitivity profile of the continuous analyte sensor(s) 140 to generate measured analyte concentration levels, and transmit the measured analyte concentration level data, including the measured analyte concentration levels, to a display device, such as display devices 107b, 107c, and / or 107d, via a wireless connection. For example, sensor electronics module 138 may be configured to 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, etc., and to 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, 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 include at least one measured analyte concentration level having an associated time tag, sequence number, etc.

[0052] In certain embodiments, continuous analyte sensor(s) 140 may incorporate a thermocouple within, or alongside, the percutaneous wire to provide an analog temperature signal to the sensor electronics module 138, 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 138 above the adhesive pad, or, alternatively, the thermocouple may contact the epidermis of the patient through openings in the adhesive pad.

[0053] In certain embodiments, the sensor electronics module 138 includes, inter alia, processor 133, storage element or memory 134, wireless transmitter / receiver (transceiver) 136, one or more antennas coupled to wireless transceiver 136, 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) 140 (such as a potentiostat), etc.

[0054] Processor 133 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 138. Processor 133 may include a single integrated circuit, such as a microprocessing device, or multiple integrated circuit devices and / or circuit boards working in cooperation to accomplish the appropriate functionality. In certain embodiments, processor 133, memory 134, wireless transceiver 136, the A / D signal processing circuitry, and the digital signal processing circuitry may be combined into a system-on-chip (SoC).

[0055] Generally, processor 133 may be configured to sample the analog electrical signal using the A / D signal processing circuitry at regular intervals (such as the sampling instant or period) to generate analyte sensor count values based on the analog electrical signals produced by the continuous analyte sensor(s) 140, calibrate the analyte sensor count values based on the sensitivity profile of the continuous analyte sensor(s) 140 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. Processor 133 may store the measured analyte concentration level data in memory 134, and generate the sensor data packages at regular intervals (such as the transmission period) for transmission by wireless transceiver 136 to a display device, such as display devices 107b, 107c, 107d, and / or 107a. Processor 133 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

[0056] In various embodiments, memory 134 may include volatile and nonvolatile medium. For example, memory 134 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-transitoiy computer-readable medium. Memory 134 may store one or more analyte sensor system applications, modules, instruction sets, etc. for execution by processor 133, such as instructions to generate measured analyte data from the analyte sensor count values, etc.

[0057] Memory 134 may also store certain sensor operating parameters 135, such as a calibration slope (or calibration sensitivity), a calibration baseline, etc. In particular, the calibrationsensitivity, calibration baseline, and other information related to the sensitivity profile for the sensor electronics module 138 may be programmed into the sensor electronics module 138 during the manufacturing process, and then used to convert the analyte sensor electrical signals into measured analyte concentration levels. For example, as discussed above, the calibration slope may be used to predict an initial in vivo sensitivity (Mo) and a final in vivo sensitivity (Mr), which are stored in memory 134 and used to convert the analyte sensor electrical signals into measured analyte concentration levels. In certain embodiments, calibration sensitivity (Mcc) 146 and / or calibration baseline 147 may be stored in memory 134.

[0058] In certain embodiments, sensor electronics module 138 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. Sensor electronics module 138 can be physically connected to continuous analyte sensor(s) 140 and can be integral with (non-releasably attached to) or releasably attachable to continuous analyte sensor(s) 140. Sensor electronics module 138 may include hardware, firmware, and / or software that enable measurement of levels of analyte(s) via continuous analyte sensor(s) 140. For example, sensor electronics module 138 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.

[0059] Display devices 107b, 107c, 107d, and / or 107a are configured for displaying displayable sensor data, including analyte data, which may be transmitted by sensor electronics module 138. Each of display devices 107b, 107c, 107d, or 107a may include a display such as a touchscreen display 109b, 109c, 109d, and / or 109a for displaying sensor data to a patient and / or for receiving inputs from the patient. For example, a graphical user interface (GUI) may be presented to the patient 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 patient of the display device and / or for receiving patient inputs. Display devices 107a, 107b, 107c, and 107d may be examples of displaydevice 107 illustrated in FIG. 1 used to display sensor data to a patient of the system of FIG. 1 and / or to receive input from the patient.

[0060] In certain embodiments, one, some, or all of the display devices are configured to 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.

[0061] The plurality of display devices may include a custom display device specially designed for displaying certain types of displayable sensor data associated with analyte data received from sensor electronics module. In certain embodiments, the plurality of display devices may be configured for providing alerts / alarms based on the displayable sensor data. Display device 107b is an example of such a custom device. In certain embodiments, one of the plurality of display devices is a smartphone, such as display device 107c 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 display device 107d which represents a tablet, display device 107a which represents a smart watch or fitness tracker, medical device 108 (e g., an insulin delivery device or a blood glucose meter), and / or a desktop or laptop computer (not shown).

[0062] 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 user, such as the patient) for each particular display device. Accordingly, in certain embodiments, a plurality of different display devices can be in direct wireless communication with a sensor electronics module (e.g., such as an on-skin sensor electronics module 138 that is physically connected to continuous analyte sensor(s) 140) 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.

[0063] As mentioned, sensor electronics module 138 may be in communication with a medical device 108. Medical device 108 may be a passive device in some example embodiments of the disclosure. For example, medical device 108 may be an insulin pump for administering insulin toa patient. For a variety of reasons, it may be desirable for such an insulin pump to receive and track lactate, glucose, ketones, glycerol and free fatty acid values transmitted from continuous analyte sensor systems 104, where continuous analyte sensor 140 is configured to measure lactate, glucose, ketones, glycerol, and / or free fatty acids.

[0064] Further, as mentioned, sensor electronics module 138 may also be in communication with other non-analyte sensors 142. Non-analyte sensors 142 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. Non-analyte sensors 142 may also include monitors such as heart rate monitors, blood pressure monitors, pulse oximeters, caloric intake monitors, indirect calorimetry devices, continuous positive airway pressure machines, and medicament delivery devices. One or more of these non-analyte sensors 142 may provide data to therapy management engine 112 described further below. In some aspects, a patient may manually provide some of the data for processing by the therapy management engine 112 of FIG. 1.

[0065] In certain embodiments, non-analyte sensors 142 may further include sensors for measuring skin temperature, core temperature, sweat rate, and / or sweat composition.

[0066] In certain embodiments, the non-analyte sensors 142 may be combined in any other configuration, such as, for example, combined with one or more continuous analyte sensors 140. As an illustrative example, a non-analyte sensor, e.g., a temperature sensor, may be combined with a continuous glucose sensor 140 to form a glucose / temperature sensor used to transmit sensor data to the sensor electronics module 138 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 140 configured to measure lactate and glucose to form a lactate / glucose / temperature sensor used to transmit sensor data to the sensor electronics module 138 using common communication circuitry.

[0067] In certain embodiments, a wireless access point (WAP) may be used to couple one or more of continuous analyte sensor system 104, the plurality of display devices, medical device(s) 108, and / or non-analyte sensor(s) 142 to one another. For example, such WAP may provide WiFi and / or cellular connectivity among these devices. Near Field Communication (NFC) and or Bluetooth may also be used among devices depicted in diagram 150 of FIG. IB.

[0068] FIG. 2 illustrates example inputs and example metrics that are generated based on the inputs in accordance with certain embodiments of the disclosure. In particular, FIG. 2 illustrates example inputs 127 on the left, application 106 and therapy management engine 112, with DAM 111, in the middle, and example outputs 130 on the right. In certain embodiments, application 106 may obtain inputs 127, in the form of time-series data, through one or more channels (e.g., continuous analyte sensor(s) 140, non-analyte sensor(s) 142, various applications executing on display device 107, etc.). Inputs 127 may be further processed by DAM 111 to output a plurality of metrics, such as outputs 130. Further, inputs (e.g., inputs 127) and metrics (e.g., outputs 130) may be used by the DAM 111 and / or any computing device in the system 100 to perform various processes. Any of inputs 127 may be used for computing any of outputs 130. In certain embodiments, each one of outputs 130 may correspond to one or more values, e.g., discrete numerical values, ranges, or qualitative values (high / medium / low or stable / unstable). In some embodiments, some or all of outputs 130 may include time-series data and / or be provided in the form of time-series data.

[0069] In certain embodiments, inputs 127 include food consumption information. Food consumption information may include 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. In certain embodiments, food consumption may be provided by the user 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 (e.g., 'three cookies'), menu items (e.g., 'Royale with Cheese'), and / or food exchanges (1 fruit, 1 dairy). In some examples, meals may also be entered with the user's typical items or combinations for this time or setting (e.g., workday breakfast at home, weekend brunch at restaurant). In some examples, meal information may be received via a convenient user interface provided by application 106.

[0070] In certain embodiments, inputs 127 include activity information. Activity information may be provided, for example, the one or more non-analyte sensors 142 of FIG. IB. In certain embodiments, activity information may additionally be provided through manual input by user 102. Activity information may include, for example, a time series for each of heart rate, activity minutes, step count, floors climbed, location information (e.g., GPS data), calories burned, sleep duration and / or quality, activity level (e.g., light, medium, or heavy), and / or similar information.In addition, or alternatively, the activity information can include one or more time series for recorded activities of one or more defined activity types (e.g., walk, run, sprint, swim, weightlift etc.), where each activity is associated with a duration and / or time period.

[0071] In certain embodiments, inputs 127 include patient statistics, such as one or more of age, height, weight, body mass index, body composition (e.g., % body fat), stature, build, or other information. Patient statistics may be provided through a user interface, by interfacing with an electronic source such as an electronic medical record, and / or from measurement devices. The measurement devices may include one or more of a wireless, e.g., Bluetooth-enabled, weight scale and / or camera, which may, for example, communicate with the display device 107 to provide patient data.

[0072] In certain embodiments, inputs 127 include information relating to the user’s medication intake. For example, the user’s medication intake may include the user’s insulin delivery. Such information may be received, via a wireless connection on a smart pen, via user input, and / or from an insulin pump (e.g., medical device 108). Insulin delivery information may include one or more of insulin volume, time of delivery, etc. Other configurations, such as insulin action time or duration of insulin action, may also be received as inputs.

[0073] In certain embodiments, inputs 127 include physiological information received from non-analyte sensor(s) 142 , which may detect one or more of heart rate, respiration, oxygen saturation, body temperature, etc. (e g., to detect illness, stress levels, etc.). In certain embodiments, inputs 127 include time, such as time of day, or time from a real-time clock.

[0074] In certain embodiments, inputs 127 include analyte data, which may be provided as input from analyte sensor system 104, for example, in any of the ways described with respect to FIG. 1A. An example of analyte data is glucose data, which may be provided and / or stored as a time series corresponding to time-stamped glucose measurements over time. Other types of analyte data, such as ketone data, potassium data, lactate data, etc., may similarly be provided and / or stored as a time series.

[0075] As described above, in certain embodiments, DAM 111 generates, determines, and / or computes outputs 130 based on inputs 127 associated with user 102. An example list of outputs 130 is illustrated in FIG. 2. In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include metabolic rate. Metabolic rate is a metric that may indicate orinclude a basal metabolic rate (e.g., energy consumed at rest) and / or an active metabolism, e.g., energy consumed by activity, such as exercise or exertion. In some examples, basal metabolic rate and active metabolism may be tracked as separate metric. In certain embodiments, the metabolic rate may be calculated by DAM 111 based on one or more of inputs 127, such as one or more of activity information, sensor input, time, user input, etc.

[0076] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include an activity level metric. The activity level metric may indicate a level of activity of the user. In certain embodiments, the activity level metric may be determined, for example based on input from an activity sensor or other physiologic sensors. In certain embodiments, the activity level metric may be calculated by DAM 111 based on one or more of inputs 127, such as one or more of activity information, physiological information, analyte data, time, user input, etc. Activity level may indicate whether the user is exercising, at rest, sleeping, etc.

[0077] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include an insulin resistance metric (also referred to herein as an “insulin resistance”). The insulin resistance metric may be determined using historical data, real-time data, or a combination thereof, and may, for example, be based upon one or more inputs 127, such as one or more of food consumption information, blood glucose information, insulin delivery information, the resulting glucose levels, etc. In certain embodiments, the insulin on board metric may be determined using insulin delivery information, and / or known or learned (e.g., from patient data) insulin time action profiles, which may account for both basal metabolic rate (e.g., update of insulin to maintain operation of the body) and insulin usage driven by activity or food consumption.

[0078] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include a meal state metric. The meal state metric may indicate the state the user is in with respect to food consumption. For example, the meal state may indicate whether the user 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.).

[0079] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include health and sickness metrics. Health and sickness metrics may be determined, for example, based on one or more of user input (e.g., pregnancy information or known sickness information), from non-analyte sensor(s) 142, such as 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, the user’s state may be defined as being one or more of healthy, ill, rested, or exhausted. In certain embodiments, health and sickness metric may indicate the user’s heart rate, stress level, etc.

[0080] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include analyte level metrics. Analyte level metrics may be determined from analyte data (e.g., glucose measurements obtained from analyte sensor system 104). In some examples, an analyte level metric may also be determined, for example, based upon historical information about analyte levels in particular situations, e.g., given a combination of food consumption, insulin, and / or activity. An analyte level metric may include a rate of change of the analyte, time in range, time spent below a threshold level, time spent above a threshold level, or the like. In certain embodiments, an analyte trend may be determined based on the analyte level over a certain period of time. As described above, example analytes may include glucose, ketones, lactate, potassium and others described herein.

[0081] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include a disease stage. For example disease stages for Type II diabetics may include a pre-diabetic stage, an oral treatment stage, and a basal insulin treatment stage. In certain embodiments, degree of glycemic control (not shown) may also be determined as an outcome metric, and may be based, for example, on one or more of glucose levels, variation in glucose level, or insulin dosing patterns.

[0082] In certain embodiments, outputs 130 generated, determined, or computed by DAM 111 include clinical metrics. Clinical metrics generally indicate a clinical state a user is in with respect to one or more conditions of the user, such as diabetes. For example, in the case of diabetes, clinical metrics may be determined based on glycemic measurements, including one or more of A1C, trends in A1C, time in range, time spent below a threshold level, time spent above a threshold level, and / or other metrics derived from glucose values. In certain embodiments, clinical metricsmay also include one or more of estimated A1C, glycemic variability, hypoglycemia, and / or health indicator (time magnitude out of target zone).

[0083] FIG. 3 illustrates an example of a process 300 for generating insulin therapy settings. The process 300 can execute at a configurable interval, such as daily, every two days, weekly, monthly, every 90 days, etc. In addition, or alternatively, the process 300 can be executed on-demand, upon a determination that a suitable glucose control metric (e.g., time in range) over a period is below a threshold value (e.g., 90 percent), etc.

[0084] In some embodiments, the process 300 can be executed, for example, by the therapy management engine 112 of FIGS. 1A-B and 2. In addition, or alternatively, the process 300 can be executed, for example, by the analyte sensor system 104 of FIGS. 1A-B. In addition, or alternatively, the process 300 can be executed, for example, by the application 106 of FIGS. 1A-B and 2. In addition, or alternatively, the process 300 can be executed generally by any of the display devices 107 of FIGS. 1A-B and 2. Although any number of systems, in whole or in part, can implement the process 300, to simplify discussion, the process 300 will be described generically in relation to the therapy management engine 112 of FIGS. 1A-B and 2.

[0085] At block 302, the therapy management engine 112 receives glucose data of a patient for each of a plurality of time periods. The glucose data can include, for example, glucose measurements for overlapping periods of different length, such as a preceding 90 days, 60 days, 30 days, 48 hours, 24 hours, combinations of the foregoing and / or the like. The glucose data can also include, for example, the patient’s typical correction factor (e.g., one unit of insulin lowers the patient’s glucose by 15 mg / dL).

[0086] At block 304, the therapy management engine 112 determines the patient’s glucose control for each of the one or more time periods based on the glucose data. For example, the therapy management engine 112 can compute a time in range, average glucose, number of highs (e g., number of glucose measurements in excess of a predetermined threshold indicative of hyperglycemia), number of lows (e.g., number of glucose measurements below a predetermined threshold indicative of hypoglycemia), a standard deviation, combinations of the forgoing and / or the like. In this way, the block 304 can include determining the patient’s glucose control for a preceding 90 days, 60 days, 30 days, 48 hours, 24 hours, and / or the like.

[0087] At block 306, the therapy management engine 112 determines insulin utilization data of the patient for each of the time periods. For example, for each of the time periods, the insulin utilization data can include an aggregate daily total for each of basal insulin administration, bolus insulin administration, and / or total insulin administration. Each aggregate daily total can be, for example, an arithmetic mean of daily totals for a given period or another statistical aggregation. In some aspects, the insulin utilization data can further include variance in each of the aggregate daily totals. For example, for each of the time periods, the insulin utilization data can indicate variance in the aggregate daily total for each of bolus insulin administration, basal administration, and / or total insulin administration. In other example, the insulin utilization data can include other statistical data reflecting differences in insulin utilization (e.g., standard deviation), and / or the like.

[0088] In certain aspects, the insulin utilization data can include time-series insulin data that is aggregated for recurring intervals of a 24-hour modal day. In an example, the modal day can include 24 one-hour intervals, 48 half-hour intervals, and / or the like. In an example, for a period covering a preceding 90 days, such time-series insulin data can indicate, for example, an average amount of insulin administered during each interval. According to this example, for an hourly interval covering 1 -2 pm, the time-series insulin data could indicate an average amount of insulin administered from 1 -2 pm over the course of the 90-day period. In another example, for a period covering a preceding 90 days, such time-series insulin data can indicate, for example, an average cumulative amount of insulin administered from a start of the day through an end of each interval. According to this example, for an hourly interval covering 1 - 2pm, the time-series insulin data could indicate an average cumulative amount of insulin administered from midnight to 2 pm. In various aspects, the insulin utilization data can include a set of time-series insulin data for each of the time periods discussed previously, such as a preceding 90 days, 60 days, 30 days, 48 hours, 24 hours, and / or the like.

[0089] In certain aspects, the insulin utilization data can include time-series basal insulin data and time-series bolus insulin data that is aggregated for recurring intervals of a 24-hour modal day, as discussed above. More particularly, the time-series basal insulin data can indicate administration of basal insulin, while the time-series bolus insulin data can indicate administration of bolus insulin. As discussed above, the time-series basal insulin data and the time series bolus insulin data can include, for example, a set of time-series data for each of the time periods discussed previously, such as a preceding 90 days, 60 days, 30 days, 48 hours, 24 hours, and / or the like. For example,for a period covering a preceding 90 days, the time-series basal insulin data can indicate, for example, an average amount of insulin administered during each interval (e.g., for a 1 - 2 pm interval, an average amount of insulin administered from 1 to 2 pm) and / or an average cumulative amount of insulin administered from a start of the day through an end of each interval (e.g., for a 1 - 2 pm interval, an average cumulative amount of insulin administered from midnight to 2 pm).

[0090] At block 308, the therapy management engine 112 determines weighted insulin utilization data for the patient across the time periods. In certain aspects, the therapy management engine 112 can weight any of the insulin utilization data described above in the block 306 to give the data more or less weight relative to other data. For example, if there is insulin utilization data for four periods (e.g., 48 hours, 30 days, 60 days, and 90 days), the therapy management engine 112 can configurably distribute weight to the data from each period. In some aspects, the therapy management engine 112 can give greater weight to more recent data. In an example, the therapy management engine 112 can determine a weighted total daily insulin, a weighted total daily basal insulin, and / or a weighted total daily bolus insulin. In another example, the therapy management engine 112 can determine a set of weighted time-series insulin data, a set of weighted time-series basal insulin data, and / or a set of weighted time-series bolus insulin data. Table 1 below shows an example distribution of 1.0 over four example periods, such that greater weight is given to more recent data.

[0091] In some aspects, the therapy management engine 112 can use different weights for different types of data. For example, as a general matter, total daily bolus variances may be greater than total daily basal variances. Accordingly, in some aspects, less recency bias can be applied to daily bolus variances than to daily basal variances. Other examples of customizing or varyingweights for different types of data will be apparent to one skilled in the art after a detailed review of the present disclosure.

[0092] At block 310, the therapy management engine 112 determines relationships between the patient’s basal and bolus insulin utilization data. In certain aspects, the therapy management engine 112 can determine one or more ratios of the patient’s total daily basal insulin to the patient’s total daily bolus insulin. In an example, the therapy management engine 112 can determine a basal / bolus ratio for each of the time periods discussed above, such as a preceding 90 days, 60 days, 30 days, 48 hours, 24 hours, and / or the like. In another example, the therapy management engine 112 can determine a weighted basal / bolus ratio, for example, as a ratio of the weighted total daily basal insulin and the weighted total daily bolus insulin, as discussed above relative to the block 308.

[0093] At block 312, the therapy management engine 112 generates treatment settings. In certain aspects, an initial execution of the block 312 can result in establishing the treatment settings. For example, the therapy management engine 112 can leverage the insulin utilization data to establish baselines for a basal rate and a target basal / bolus ratio (e.g., according to the weighted basal / bolus ratio), both of which can be adjusted after monitoring. In some aspects, if no insulin utilization data is available during an initial execution of the block 312 (e.g., due to the patient beginning insulin pump therapy), the baselines can be established using a combination of weightbased metrics (e.g., approximately .55 units / kg of body weight) and a default starting ratio (e.g., an initial 50 / 50 basal / bolus ratio). In addition, or alternatively, in an initial execution of the block 312, the baselines for basal rate and target basal / bolus ratio can be established via input from the patient and / or a medical professional.

[0094] In certain aspects, subsequent executions of the block 312 can involve, for example, the therapy management engine 112 adjusting an existing set of treatment settings. For example, the therapy management engine 112 can determine to make an adjustment to a basal rate and / or a target basal / bolus ratio if less than a configurable percentage of the patient’s insulin is delivered by bolus (e.g., 65%, 75%, 100%, etc., according to an aspirational basal / bolus ratio) and defined glucose control criteria is satisfied. In various examples, the defined glucose control criteria can be satisfied, for example, if a time in range is less than a threshold value (e.g., 90%), an average glucose is greater than a defined threshold value (e.g., 120 mg / dL), a standard deviation of glucoselevels is less than a defined threshold value (e.g., 30, 45, etc.) combination of thresholds similar to the foregoing are satisfied, and / or the like.

[0095] In some aspects, the adjustments to the treatment settings, if applicable, can include an incremental adjustment to the target basal / bolus ratio. In an example, if the target basal / bolus ratio is currently 50 / 50, the therapy management engine 112 can adjust the target basal / bolus ratio towards an aspirational ratio by a specified amount, such as 10% (e g., to a 40 / 60 basal / bolus ratio).

[0096] In some aspects, the adjustments to the treatment settings, if applicable, can include an incremental adjustment to the current basal rate. For example, the incremental adjustment can be expressed as units per hour (e.g., 0.1 U / h, 0.2 U / h), a percentage (e.g., 10 percent adjustment), etc. In some aspects, the incremental adjustment to the treatment settings, if applicable, can be expressed with reference to the target basal / bolus ratio (e.g., 35 / 65, 25 / 75, 0 / 100, etc.). For example, if the target basal / bolus ratio is 35 / 65, and the weighted basal / bolus ratio from previous period(s) is 50 / 50, the therapy management engine 112 can adjust the basal rate in correspondence to a 10% adjustment (i.e., 40 / 60).

[0097] In some aspects, greater adjustments can be made in response to a lesser degree of glucose control, as indicated by the glucose control metrics. For example, for a patient with an average glucose above a predefined threshold (e.g., 175 mg / dL) and a time in range below a predetermined threshold (e.g., 40%), a multiple can be applied to the incremental adjustment to basal rate mentioned above. In some aspects, greater adjustments to basal rate can be made based on a current variation from a target basal / bolus ratio. For example, for a target basal / bolus ratio of 35 / 65, a greater incremental adjustment can be made for a weighted basal bolus ratio of 50 / 50 than for 40 / 60. In some aspects, the therapy management engine 112 can stop adjusting treatment settings, or determine not to make an adjustment to treatment settings, once a predetermined time in range is achieved (e.g., 90%, 96%, etc.).

[0098] At block 314, the therapy management engine 112 stores insulin therapy settings, for example, in memory accessible to thereto (e.g., shown as insulin therapy settings 640 in FIG. 6). In general, the insulin therapy settings can include, for example, any of the data determined or generated during the process 300. The insulin therapy settings can include, for example, the glucose control determined at the block 304, the insulin utilization data determined at the block 306, the weighted insulin utilization data determined at the block 308, the relationships determinedat the block 310, the treatment settings generated at the block 312, combinations of the foregoing and / or the like. After block 314, the process 300 ends.

[0099] In some implementations, the system constructs an insulin profile for the patient by aggregating insulin delivery records, including basal insulin delivered over time and discrete bolus events, across a time period (e.g., 1, 5, 10, 15, 30, 60, 90 days, etc.). For each day and optionally for time-of-day segments, the system can computes central tendencies such as average total daily dose, basal-to-bolus distribution, and time-of-day dose expectations; dispersion measures such as variance or standard deviation for basal and bolus quantities; and recency weighting that privileges more recent days according to a decay function. The profile can establish expectations for the fraction of total daily dose delivered by specific times of day and the expected ratio between basal and bolus components. In some implementations, the system can target a basal / bolus distribution of certain percentages (e.g., 10% / 90% basal / bolus, 20% / 80% basal / bolus, 30% / 70% basal / bolus, 35% / 65% basal / bolus, 40% / 60% basal / bolus, 45% / 55% basal / bolus, etc.), enabling gradual automation toward this target when consistent with the learned profile and safety criteria.

[0100] In some implementations, the system can be configured to generate a current-day assessment. For example, beginning at a certain time of day (e.g., midnight) and recurring periodically (e.g., every 5, 10, 15, 30, 60, 90, etc. minutes), the system can aggregate current-day insulin statistics, including basal delivered to date, bolus delivered to date, and total delivered dose. The system can map the elapsed time of day to the historical profile and compute expected values for total dose, basal, and bolus for the current time block. Deviations can be quantified as variance ratios or differences, such as the ratio of current-day bolus delivered to expected bolus for the elapsed time.

[0101] In some implementations, the system can be configured to determine bolus baseline and confidence scores. The system can compute a baseline bolus derived, for example, from a correction factor, current glucose measurements, and target glucose. The baseline can be represented as a difference between current glucose and target glucose normalized by the correction factor, for instance:&where Gcun-ent is the measured glucose, Gtarget is the desired glucose level, and CF is the correction factor. The system can also consider glucose trend information, such as rate and direction of change, to optionally augment the baseline for rapid rises.

[0102] A bolus correction confidence score can be computed as a function of weighted metrics that reflect both historical expectations and current-day behavior. The metrics can include, without limitation, the traditional correction factor, a current-day basal-to-bolus ratio compared with a target distribution and historical expectation, total basal variance ratio across the historical window with recency bias, total bolus variance ratio across the historical window with recency bias, a current-day basal-to-expected daily basal ratio, a current-day bolus-to-expected daily bolus ratio, a total dose today-to-expected total daily dose variance ratio adjusted for the elapsed fraction of the day, and / or an average bolus dose metric derived from historical bolus distributions. The weights assigned to these metrics can be adaptively modified according to the comparison of current-day behavior to the expected daily behavior. For example, if the current day shows substantial under-delivery of bolus relative to expectation by the time of day, the system can increase the weight of the current-day bolus ratio metric relative to the traditional correction, thereby increasing the final recommended bolus.

[0103] In some implementations, the system can be configured to generate therapy instructions and safety constraints. The adjusted bolus recommendation can be derived by modulating the baseline bolus with the confidence score. The system can enforce safety thresholds that cap the adjusted recommendation based, for example, on one or more of variance-aware bounds derived from the historical profile, insulin-on-board constraints, time-of-day sensitivities, and / or glucose trend-based considerations. In some implementations, the system can limit the incremental increase above the baseline bolus to the detected shortfall in expected bolus delivery for the elapsed portion of the day. The system may further apply conservative bounds during periods of greater variability or when the recency-weighted profile indicates instability. The instruction can be delivered automatically or provided to the patient or clinician for confirmation, and the loop can repeat at the configured interval to enable frequent, modest corrections.

[0104] Illustrative Example. Consider a patient at approximately 4 pm (sixteen hours into the day) with glucose level of 270 mg / dL and a rapid upward trend. With a correction factor of 15 mg / dL per unit and a target of 110 mg / dL, the baseline bolus would be approximately:270 - 11010.6 units

[0105] In this example, the historical profile indicates a typical total daily dose of about 66 units comprising approximately 23 units basal and 43 units bolus. At two-thirds of the day, the expected total delivered dose is approximately 44 units with an expected bolus of about 28.6 units, yet the patient has delivered only 14 units basal and 13 units bolus, totaling 27 units. The system identifies a shortfall in bolus delivery of approximately 15.6 units relative to the expectation for the elapsed time. Given the elevated glucose and rising trend, the confidence score reduces reliance on the traditional correction alone and increases the recommendation above the baseline, for example toward the shortfall, while capping the bolus at or below the shortfall-based bound. The resulting insulin therapy instruction falls between approximately 10.6 and 15.6 units, refraining from exceeding the upper bound and providing a variance-aware adjustment aligned with the patient’s historical and current-day behavior.

[0106] In some implementations, the system can incorporate machine learning techniques. For example, the construction of the insulin profile, the computation of variance and recency-weighted expectations, the calculation of the bolus correction confidence score, and the adaptation of weights over time can be realized using machine learning techniques. Suitable methods include supervised learning models such as linear regression and logistic regression for continuous and categorical predictions, decision trees and random forests for non-linear relationships and robustness to noise, support vector machines for margin-based classification tasks, k-nearest neighbors and naive Bayes for pattern recognition, unsupervised methods including k-means and hierarchical clustering for identifying latent dose patterns, dimensionality reduction techniques such as principal component analysis to extract dominant behavioral components, autoencoders for representation learning, sequential models such as recurrent neural networks, long short-term memory networks, and transformers to capture temporal dependencies across time-of-day and across days, and reinforcement learning methods including Q-learning, SARSA, policy gradients, and deep Q-networks to optimize a dosing policy subject to safety and bounded action constraints. In some implementations, sequential models learn time-of-day sensitivity and recency effects, while reinforcement learning refines the policy for adjusting weights and thresholds based on outcomes.

[0107] In some implementations, the system can be configured to implement personalization and adaptation. For example, the system can initialize with default weights and expectations, and transition to patient-specific values as sufficient historical data accrues. Recency bias can enable faster adaptation to changes in routine or physiology by assigning larger weights to recent days. Weight updates may occur continuously or at defined checkpoints, subject to data sufficiency, stability criteria, and safety verifications. Over time, the system can converge toward individualized dosing behavior, including gradual automation toward target basal / bolus ratios when supported by the profile and clinical constraints.

[0108] In some implementations, the system can be configured for specific safety, compliance, and clinical considerations. For example, patient safety can be preserved through conservative bounds on bolus adjustments, insulin-on-board tracking, and trend-aware safeguards. The system can incorporate lockouts or reduced adjustments during periods of rapid glycemic change, suspected sensor anomalies, or insufficient data quality. Clinical parameters such as minimum and maximum bolus increments, time-to-peak insulin activity, and hypoglycemia risk indicators can be integrated into the threshold logic. The system’s decisions can be logged and audited, supporting clinical oversight and iterative improvement.

[0109] The disclosed algorithms may run on-device in a pump controller or on a mobile application interfaced with a pump via secure wireless communication. The system may also connect to cloud services for data storage, model training, and remote monitoring. In fully closed-loop embodiments, the controller can instruct delivery of boluses automatically within established safety bounds. In advisory embodiments, the system presents therapy instructions to the user for acceptance.

[0110] The system offers improved personalization by basing bolus recommendations on historical and current-day insulin behavior rather than carbohydrate counting, enabling variance-aware, recency -weighted decisions that reflect the patient’s lifestyle. Frequent, bounded adjustments can enhance responsiveness to real-time glycemic trends while mitigating risk, and the flexibility to incorporate a range of machine learning models allows the system to evolve with accumulating patient data. By recognizing when a day’s insulin delivery lags expected behavior and cautiously increasing bolus recommendations, the system can help patients attain improved glycemic control across diverse conditions.

[0111] FIG. 4 illustrates an example of a process 400 for executing real-time insulin therapy for a patient based on insulin therapy settings that are generated, for example, as discussed relative to FIG. 3. The process 400 can execute on a configurable interval (e.g., every 20 minutes, every 30 minutes, every 60 minutes, etc.). In some aspects, the process 400 can execute at least hourly. In certain aspects, the process 400 can result in decisions on small boluses being automatically made each interval, with such small being automatically administered throughout the day (e.g., 20-30 boluses).

[0112] In some embodiments, the process 400 can be executed, for example, by the therapy management engine 112 of FIGS. 1A-B and 2. In addition, or alternatively, the process 400 can be executed, for example, by the analyte sensor system 104 of FIGS. 1A-B. In addition, or alternatively, the process 400 can be executed, for example, by the application 106 of FIGS. 1A-B and 2. In addition, or alternatively, the process 400 can be executed generally by any of the display devices 107 of FIGS. 1A-B and 2. Although any number of systems, in whole or in part, can implement the process 400, to simplify discussion, the process 400 will be described generically in relation to the therapy management engine 112 of FIGS. 1 A-B and 2.

[0113] At block 402, the therapy management engine 112 receives glucose data of the patient for a current period, such as a current day or the last 24 hours. The glucose data can include, for example, glucose measurements generated by the analyte sensor system 104. The glucose data can further include, for example, the patient’s typical correction factor (e.g., one unit of insulin lowers the patient’s glucose by 15 mg / dL).

[0114] At block 404, the therapy management engine 112 determines the patient’s insulin utilization data for the current period. The insulin utilization data can include, for example, total insulin administered during the current period (e.g., a current day or last 24 hours), total basal insulin administered during the current period, total bolus insulin administered during the current period and / or the like. In additional, or alternatively, the insulin utilization data can include, for example, total insulin administered during one or more intervals of the current period (e.g., the last hour, the last three hours, etc.), total basal insulin administered during the interval(s) of the current period, total bolus insulin administered during the interval(s) of the current period and / or the like.

[0115] At block 406, the therapy management engine 112 determines relationships between the patient’s basal and bolus insulin utilization data for the current period. In certain aspects, thetherapy management engine 112 can determine one or more basal / bolus ratios for the current period (e.g., the current day or the last 24 hours) and / or any of the interval(s) of the current period mentioned above (e.g., the last hour, the last three hours, etc.). In an example, the therapy management engine 112 can determine a ratio of the patient’s total basal insulin administered during the current period to the patient’ s total bolus insulin administered during the current period. In another example, for each interval of the current period (and / or any combination of intervals), the therapy management can determine a ratio of the patient’s total basal insulin administered during the interval to the patient’s total bolus insulin administered during the interval.

[0116] At block 408, the therapy management engine 112 generates comparative insulin utilization data relative to previously generated insulin therapy settings for the patient. The insulin therapy settings can be generated and stored, for example, as described with respect to FIG. 3.

[0117] In an example, the therapy management engine 112 can generate a metric comparing a total insulin administered during a current interval (or combination of intervals) of the current period to a weighted total insulin administered for a corresponding interval (or combination of intervals) of a modal day, for example, according to a weighted time-series of insulin data. The weighted time-series of insulin data can result, for example, from the most recent execution of the block 308 during the process 300 of FIG. 3. For instance, if the current interval covers 1 to 2 pm of the current day, the metric can compare an amount of insulin administered from 1 to 2 pm of the current day to an average amount of insulin administered from 1 to 2 pm in the weighted timeseries of insulin data. The metric can be, for example, a difference between the two amounts.

[0118] In another example, the therapy management engine 112 can generate a metric comparing a cumulative total insulin administered during the current period to a cumulative total insulin for a corresponding period of a modal day, for example, according to the weighted timeseries of insulin data. For instance, if the current period covers midnight to 2 pm of the current day, the metric can compare a cumulative amount of insulin administered from midnight to 2 pm of the current day to a cumulative amount of insulin administered from midnight to 2 pm in the weighted time-series of insulin data. The metric can be, for example, a difference between the two amounts. In some aspects, the block 408 can include computing the cumulative amount of insulin indicated by the weighted time-series of insulin data. For example, if the current period is a preceding 24 hours covering 1 pm of the previous day to 1 pm of the current day, the therapymanagement engine 112 can compute the cumulative amount by aggregating (e g., summing) amounts from each corresponding interval of the modal day. In other aspects, such values can be precomputed and stored with the insulin therapy settings.

[0119] In another example, the therapy management engine 112 can generate a metric comparing a total basal insulin administered during a current interval (or combination of intervals) of the current period to a weighted total basal insulin for a corresponding interval (or combination of intervals) of a modal day, for example, according to a weighted time-series of insulin data. The weighted time-series of insulin data can result, for example, from the most recent execution of the block 308 during the process 300 of FIG. 3. For instance, if the current interval covers 1 to 2 pm of the current day, the metric can compare an amount of basal insulin administered from 1 to 2pm of the current day to an average amount of basal insulin administered from 1 to 2 pm in the weighted time-series of insulin data. The metric can be, for example, a difference between the two amounts.

[0120] In another example, the therapy management engine 112 can generate a metric comparing a cumulative total basal insulin administered during the current period to a cumulative total basal insulin for a corresponding period of a modal day, for example, according to the weighted time-series of insulin data. For instance, if the current period covers midnight to 2 pm of the current day, the metric can compare a cumulative amount of basal insulin administered from midnight to 2 pm of the current day to a cumulative amount of basal insulin administered from midnight to 2 pm in the weighted time-series of insulin data. The metric can be, for example, a difference between the two amounts.

[0121] In another example, the therapy management engine 112 can generate a metric comparing a total bolus insulin administered during a current interval (or combination of intervals) of the current period to a weighted total bolus insulin for a corresponding interval (or combination of intervals) of a modal day, for example, according to a weighted time-series of insulin data. The weighted time-series of insulin data can result, for example, from the most recent execution of the block 308 during the process 300 of FIG. 3. For instance, if the current interval covers 1 to 2 pm of the current day, the metric can compare an amount of bolus insulin administered from 1 to 2pm of the current day to an average amount of bolus insulin administered from 1 to 2 pm in theweighted time-series of insulin data. The metric can be, for example, a difference between the two amounts.

[0122] In another example, the therapy management engine 112 can generate a metric comparing a cumulative total bolus insulin administered during the current period to a cumulative total bolus insulin for a corresponding period of a modal day, for example, according to the weighted time-series of insulin data. For instance, if the current period covers midnight to 2 pm of the current day, the metric can compare a cumulative amount of bolus insulin administered from midnight to 2 pm of the current day to a cumulative amount of bolus insulin administered from midnight to 2 pm in the weighted time-series of insulin data. The metric can be, for example, a difference between the two amounts.

[0123] At block 410, the therapy management engine 112 generates an insulin therapy instruction, for example, based on the insulin therapy settings and the comparative insulin utilization data generated at the block 408. In certain aspects, the insulin therapy instruction can result from a real-time determination based on the comparative insulin utilization metrics. In certain aspects, the insulin therapy instruction can represent a determination of whether to administer insulin by bolus and / or a total bolus amount.

[0124] For example, if the glucose data indicates a current glucose level in excess of one or more thresholds (e.g., over 100 mg / dL), and the cumulative total insulin administered during the current period is less than the cumulative total insulin for a corresponding period of a modal day (e g., with reference to the metrics generated at the block 408), the therapy management engine 112 can determine to calculate a total bolus amount and / or to administer a bolus. According to this example, the therapy management engine 112 can calculate a total bolus amount that is different than the amount that would be calculated, for example, solely based on the patient’s typical correction factor.

[0125] In some aspects, the total bolus amount can reflect an incremental increase to an amount calculated based on the patient’s typical correction factor, such as one unit, a half unit, and / or the like. In certain aspects, the incremental increase can correspond, for example, to a non-calculated incremental amount that is configured and customized to the patient. In certain aspects, greater adjustments can be determined for greater differences in the comparative insulin utilization metrics. For example, if the patient has usually taken 20 units of insulin by a given point in the daybut has only taken 4 units by that point, and the patient’s glucose levels are currently 200 mg / dL, a multiple can be applied to the incremental increase (e.g., 2, 3, etc.).

[0126] In some aspects, the therapy management engine 112 can calculate a separate bolus amount that is independent of any bolus amount calculated by the other therapy algorithm. In some of these aspects, the separate bolus amount can be used as the total bolus amount. In addition, or alternatively, the separate bolus amount can be averaged with the other bolus amount to determine the total bolus amount. For example, the total bolus amount can be a weighted average of the two amounts. Further examples of generating the insulin therapy instruction will be described in greater detail relative to FIG. 5.

[0127] At block 412, the therapy management engine 112 commands an insulin delivery device (e.g., an insulin pump) based on the insulin therapy instruction. The insulin deliver device can be, for example, the medical device 108 of FIG. 1A. In some aspects, if the insulin therapy instruction indicates a total bolus amount of zero, the block 412 can involve the therapy management engine 112 not issuing any commands to the insulin delivery device (e.g., commands to deliver insulin). In some aspects, if the insulin therapy instruction indicates a non-zero total bolus amount, the therapy management engine 112 can command the insulin delivery device to deliver the total bolus amount to the patient. After block 412, the process 400 ends.

[0128] As discussed previously, the process 400 can execute on a configurable interval (e.g., every 20 minutes, every 30 minutes, every 60 minutes, etc.). In some aspects, the process 400 can execute at least hourly. In certain aspects, this repeated or continuous execution of the process 400 can result in smaller, more frequent boluses as compared conventional insulin therapy that uses, for example, a 50 / 50 split between basal and bolus insulin administration. In certain aspects, such smaller, more frequent boluses can improve patient health. In an example, if a patient is subject to five 4-unit boluses in a given day under conventional insulin therapy, according to various approaches described herein, the same patient can instead be subject to twenty one-unit boluses over the same time period. Advantageously, in certain aspects, more frequent, smaller bolus doses can cause a more rapid reaction to rising glucose levels, thereby improving time in range, lowering an average glucose, reducing a risk and frequency of hyperglycemia, and improving the patient’s A1C. Further, in certain aspects, more frequent, smaller bolus doses can reduce an amount andseverity of overcorrections that cause, for example, extra glucose intake to correct or prevent hypoglycemia.

[0129] FIG. 5 illustrates an example of a process 500 for generating an insulin therapy instruction. The process 500 can be executed, for example, as part of the block 410 of FIG. 4. In some embodiments, the process 500 can be executed, for example, by the therapy management engine 112 of FIGS. 1A-B and 2. In addition, or alternatively, the process 500 can be executed, for example, by the analyte sensor system 104 of FIGS. 1A-B. In addition, or alternatively, the process 500 can be executed, for example, by the application 106 of FIGS. 1 A-B and 2. In addition, or alternatively, the process 500 can be executed generally by any of the display devices 107 of FIGS. 1A-B and 2. Although any number of systems, in whole or in part, can implement the process 500, to simplify discussion, the process 500 will be described generically in relation to the therapy management engine 112 of FIGS. 1A-B and 2.

[0130] At block 502, the therapy management engine 112 determines a first bolus amount according to another therapy algorithm, such as an algorithm that calculates the first bolus amount according to a correction factor. The other therapy algorithm may calculate the first bolus amount to achieve a treatment objective (e.g., a target glucose level (e.g., 100 mg / dL). The correction factor can be, for example, the patient’s typical correction factor as discussed above relative to FIG. 4 (e.g., one unit of insulin lowers the patient’s glucose by 15 mg / dL). In an example scenario, if the patient’s glucose level is 160 mg / dL at 3 pm and the patient has a basal rate of 1.25 units per hour, the other therapy algorithm may utilize the correction factor of 15 mg / dL to calculate a 4-unit bolus as the first bolus amount.

[0131] At block 504, the therapy management engine 112 determines a second bolus amount based on insulin therapy settings that are generated, for example, as discussed relative to FIG. 3. The therapy management engine 112 can be programmed or associated with one or more sets of predefined criteria for determining the second bolus amount. In certain aspects, the second bolus amount can be zero if no criteria for a non-zero bolus amount is satisfied. With respect to non-zero bolus amounts, as discussed relative to FIG. 4, the second bolus amount can be a non-calculated incremental amount (e.g., one unit, a half unit, etc.) or a separately calculated amount. In certain aspects, a treatment objective of the block 504 can be to achieve a target basal / bolus ratio indicated by the insulin therapy settings, such as 35 / 65.

[0132] For instance, example predefined criteria can specify a non-zero bolus if (1) the cumulative total basal insulin for the current day is less than or equal to an amount indicated by the basal rate specified in the insulin therapy settings; and (2) the cumulative total bolus insulin for the current day is less than or equal to a calculated cumulative total bolus insulin according to the basal rate and a target basal / bolus ratio. If the predefined criteria is satisfied, the therapy management engine 112 can determine the second bolus amount based on the target basal / bolus ratio. Other example predefined criteria can relate, for example, to cumulative total insulin administered, comparisons to variances or standard deviations, and / or the like.

[0133] If the above example predefined criteria is applied to the example scenario outlined above relative to the block 502, the therapy management engine 112 can determine whether the cumulative total basal insulin for the current day is less than or equal to 18.75 units (e.g., based on the above-noted example basal rate of 1.25 units per hour) and whether the cumulative total bolus insulin for the current day is less than or equal to 34.82 units (e.g., according to the example target basal / bolus ratio of 35 / 65). If, for example, the cumulative total basal insulin for the current day is 15 units, and the cumulative total bolus insulin for the current day is 20 units, the therapy management engine 112 can determine the second bolus amount based on the example target basal / bolus ratio, which amount may be approximately 8 units in this example.

[0134] At block 506, the therapy management engine 112 determines a total bolus amount based on the first bolus amount (from block 502) and the second bolus amount (from block 504). As discussed previously, in some aspects, the second bolus amount can be a non-calculated incremental amount, such as a unit or a half-unit. In such cases, the total bolus amount can be a sum of the first and second bolus amounts.

[0135] In certain aspects, as discussed above, the second bolus amount can be a separately calculated amount, for example, based on the target basal / bolus ratio. In some of these aspects, the total bolus amount can correspond to the second bolus amount. In addition, or alternatively, in some aspects, the therapy management engine 112 can average the first and second bolus amounts. Continuing the foregoing example, if the first bolus amount is a 4 units and the second bolus amount is 8 units, a resultant bolus determination can be, for example, 6 units based on an average of the two bolus amounts. In some aspects, the therapy management engine 112 can use a weighted average to give different weights to amounts determined via the correction factor and the targetbasal / bolus ratio. Other examples of determining a bolus amount will be apparent to one skilled in the art after a detailed review of the present disclosure.

[0136] After block 506, the process 500 ends.

[0137] FIG. 6 is a block diagram depicting a computer system 600 configured for adjusting insulin therapy based on current insulin utilization, for example, according to certain embodiments disclosed herein. Although depicted as a single physical device, in embodiments, the computer system 600 may be implemented using virtual device(s), and / or across a number of devices, such as in a cloud environment and / or via separate modules of portable or cloud devices. As illustrated, the computer system 600 includes a processor 605, a memory 610, a storage 615, a network interface 625, and one or more I / O interfaces 620. In the illustrated embodiment, the processor 605 retrieves and executes programming instructions stored in the memory 610, as well as stores and retrieves application data residing in the storage 615. The processor 605 is generally representative of a single CPU and / or GPU, multiple CPUs and / or GPUs, a single CPU and / or GPU having multiple processing cores, and the like.

[0138] The memory 610 is generally included to be representative of a random access memory (RAM). The storage 615 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).

[0139] In some embodiments, VO devices 635 (such as keyboards, monitors, etc.) can be connected via the I / O interface(s) 620. Further, via the network interface 625, the computer system 600 can be communicatively coupled with one or more other devices and components, such as the user database 110. In certain embodiments, the computer system 600 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 605, memory 610, storage 615, network interface(s) 625, and the VO interface(s) 620 are communicatively coupled by one or more interconnects 630. In certain embodiments, the computer system 600 is representative of the display device 107 associated with the user. In certain embodiments, as discussed above, thedisplay device 107 can include the user’s laptop, computer, smartphone, and the like. In another embodiment, the computer system 600 is a server executing in a cloud environment.

[0140] In the illustrated embodiment, the storage 615 includes the user profile 118 and insulin therapy settings 640. The insulin therapy settings 640 can correspond, for example, to any of the insulin therapy settings discussed above relative to FIGS. 3-5. The memory 610 includes the therapy management engine 112. The therapy management engine 112 can be executed by the computer system 600 to perform operations, for example, of the process 300 of FIG. 3, the process 400 of FIG. 4, and / or the process 500 of FIG. 5.Example Clauses

[0141] Implementation examples are described in the following numbered clauses:

[0142] Clause 1: A system for automatically adjusting insulin therapy for a patient, comprising: a continuous glucose monitoring (CGM) sensor system configured to generate one or more glucose measurements associated with a current glucose level of a patient over a period; one or more memories comprising executable instructions; and one or more processors in data communication with the one or more memories and configured to execute the executable instructions to execute a process for automatically adjusting insulin therapy for the patient, the process comprising: receiving the one or more glucose measurements generated by the CGM sensor system; determining basal insulin data and bolus insulin data of the patient for the period; determining a relationship between the basal insulin data and the bolus insulin data; and generating an insulin therapy instruction for the patient based on the one or more glucose measurements and the determined relationship between the basal insulin data and the bolus insulin data, wherein the insulin therapy instruction comprises a total bolus amount.

[0143] Clause 2: The system of Clause 1, wherein the process for automatically adjusting insulin therapy executes at least hourly.

[0144] Clause 3: The system of Clause 1, the process further comprising commanding an insulin delivery device based on the insulin therapy instruction.

[0145] Clause 4: The system of Clause 1, wherein the one or more processors are further configured to execute the executable instructions to generate insulin therapy settings based onweighted insulin utilization data for the patient over a plurality of time periods, wherein the insulin therapy instruction is further based on the insulin therapy settings.

[0146] Clause 5: The system of Clause 4, wherein the generation of insulin therapy settings comprises: determining glucose data of the patient for the plurality of time periods; determining glucose control of the patient for the plurality of time periods; determining weighted insulin utilization data for the patient across the plurality of time periods, the weighted insulin utilization data comprising weighted basal insulin utilization data and weighted bolus insulin utilization data; determining one or more relationships between the weighted basal insulin utilization data and the weighted bolus insulin utilization data; and generating treatment settings for the patient based on the glucose control, the weighted insulin utilization data, and the determined one or more relationships, the insulin therapy settings comprising the treatment settings.

[0147] Clause 6: The system of Clause 5, wherein: the determination of the glucose control of the patient comprises computing at least one time in range; and the generation of the treatment settings is based on a determination that the at least one time in range is less than a threshold value.

[0148] Clause 7: The system of Clause 5, wherein: the determination of the glucose control of the patient comprises computing at least one average glucose; and the generation of the treatment settings is based on a determination that the at least one average glucose is greater than a threshold value.

[0149] Clause 8: The system of Clause 7, wherein the treatment settings comprise a target basal / bolus ratio and a basal rate.

[0150] Clause 9: The system of Clause 8, wherein the generating treatment settings comprises adjusting the target basal / bolus ratio and the basal rate.

[0151] Clause 10: The system of Clause 8, wherein: the generation of the insulin therapy instruction comprises determining the total bolus amount based on the target basal / bolus ratio and the basal rate; and the process further comprises commanding an insulin delivery device based on the determined total bolus amount.

[0152] Clause 11: The system of Clause 8, wherein the generation of the insulin therapy instruction comprises: determining a first bolus amount based on a correction factor for the patient;determining a second bolus amount based on the target basal / bolus ratio and the basal rate; and determining the total bolus amount based on the first bolus amount and the second bolus amount.

[0153] Clause 12: The system of Clause 11, wherein the determination of the total bolus amount comprises averaging the first bolus amount and the second bolus amount.

[0154] Clause 13: The system of Clause 11, wherein the determination of the total bolus amount comprises summing the first bolus amount and the second bolus amount.

[0155] Clause 14: The system of Clause 11, wherein the second bolus amount is a noncalculated incremental amount of insulin.

[0156] Clause 15: The system of Clause 11, wherein the determination of the second bolus amount comprises calculating the second bolus amount based on a cumulative amount of basal insulin administered for the period, a cumulative amount of bolus insulin administered for the period, the basal rate, and the target basal / bolus ratio.

[0157] Clause 16: The system of Clause 11, wherein the second bolus amount is non-zero responsive to determinations by the one or more processors that: the glucose data indicates that the current glucose level is in excess of a threshold; and a cumulative insulin administered during the period is less than a cumulative insulin indicated in the insulin therapy settings for a corresponding period.

[0158] Clause 17: The system of Clause 11, wherein the second bolus amount is non-zero responsive to determinations by the one or more processors that: the glucose data indicates a current glucose level in excess of a threshold; a cumulative total basal insulin for the period is less than an amount indicated by the basal rate; and a cumulative total bolus insulin for the period is less than a calculated cumulative total bolus insulin for the period, wherein the calculated cumulative total bolus insulin for the period is calculated according to the cumulative total basal insulin and the target basal / bolus ratio.

[0159] Clause 18: The system of Clause 1, wherein the total bolus amount is zero.

[0160] Clause 19: A method of automatically adjusting insulin therapy for a patient, comprising: receiving, at a device associated with the patient, one or more glucose measurements generated by a continuous glucose monitoring (CGM) sensor of the patient for a period; determining, at the device, basal insulin data and bolus insulin data of the patient for the period;determining, at the device, a relationship between the basal insulin data and the bolus insulin data; and generating, at the device, an insulin therapy instruction for the patient based on the one or more glucose measurements and the determined relationship between the basal insulin data and the bolus insulin data, wherein the insulin therapy instruction comprises a total bolus amount.

[0161] Clause 20: The method of Clause 19, the method further comprising commanding an insulin delivery device based on the insulin therapy instruction.

[0162] Clause 21: The method of Clause 19, further comprising generating insulin therapy settings based on weighted insulin utilization data for the patient over a plurality of time periods, wherein the insulin therapy instruction is further based on the insulin therapy settings.

[0163] Clause 22: The method of Clause 21, wherein the generating insulin therapy settings comprises: determining glucose data of the patient for the plurality of time periods; determining glucose control of the patient for the plurality of time periods; determining weighted insulin utilization data for the patient across the plurality of time periods, the weighted insulin utilization data comprising weighted basal insulin utilization data and weighted bolus insulin utilization data; determining one or more relationships between the weighted basal insulin utilization data and the weighted bolus insulin utilization data; and generating treatment settings for the patient based on the glucose control, the weighted insulin utilization data, and the determined one or more relationships, the insulin therapy settings comprising the treatment settings.

[0164] Clause 23: The method of Clause 22, wherein: the determining glucose control of the patient comprises computing at least one time in range; and the generating treatment settings is based on a determination that the at least one time in range is less than a threshold value.

[0165] Clause 24: The method of Clause 22, wherein: the determining glucose control of the patient comprises computing at least one average glucose; and the generating treatment settings is based on a determination that the at least one average glucose is greater than a threshold value.

[0166] Clause 25: The method of Clause 24, wherein the treatment settings comprise a target basal / bolus ratio and a basal rate.

[0167] Clause 26: The method of Clause 25, wherein the generating treatment settings comprises adjusting the target basal / bolus ratio and the basal rate.

[0168] Clause 27: The method of Clause 25, wherein: the generating the insulin therapy instruction comprises determining the total bolus amount based on the target basal / bolus ratio and the basal rate; and the method further comprises commanding an insulin delivery device based on the determined total bolus amount.

[0169] Clause 28: The method of Clause 25, wherein the generating the insulin therapy instruction comprises: determining a first bolus amount based on a correction factor for the patient; determining a second bolus amount based on the target basal / bolus ratio and the basal rate; and determining the total bolus amount based on the first bolus amount and the second bolus amount.

[0170] Clause 29: The method of Clause 28, wherein the determining the total bolus amount comprises averaging the first bolus amount and the second bolus amount.

[0171] Clause 30: The method of Clause 28, wherein the determining the total bolus amount comprises summing the first bolus amount and the second bolus amount.

[0172] Clause 31: The method of Clause 28, wherein the second bolus amount is a noncalculated incremental amount of insulin.

[0173] Clause 32: The method of Clause 28, wherein the determining the second bolus amount comprises calculating the second bolus amount based on a cumulative amount of basal insulin administered for the period, a cumulative amount of bolus insulin administered for the period, the basal rate, and the target basal / bolus ratio.

[0174] Clause 33: The method of Clause 28, wherein the second bolus amount is non-zero responsive to determinations that: the glucose data indicates that a current glucose level is in excess of a threshold; and a cumulative insulin administered during the period is less than a cumulative insulin indicated in the insulin therapy settings for a corresponding period.

[0175] Clause 34: The method of Clause 28, wherein the second bolus amount is non-zero responsive to determinations that: the glucose data indicates a current glucose level in excess of a threshold; a cumulative total basal insulin for the period is less than an amount indicated by the basal rate; and a cumulative total bolus insulin for the period is less than a calculated cumulative total bolus insulin for the period, wherein the calculated cumulative total bolus insulin for the period is calculated according to the cumulative total basal insulin and the target basal / bolus ratio.

[0176] Clause 35: The method of Clause 19, wherein the total bolus amount is zero.

[0177] Clause 36: A computer-program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed to implement a method of automatically adjusting insulin therapy for a patient, the method comprising: receiving one or more glucose measurements generated by a continuous glucose monitoring (CGM) system of the patient for a period; determining basal insulin data and bolus insulin data of the patient for the period; determining a relationship between the basal insulin data and the bolus insulin data; and generating an insulin therapy instruction for the patient based on the one or more glucose measurements and the determined relationship between the basal insulin data and the bolus insulin data, wherein the insulin therapy instruction comprises a total bolus amount.

[0178] Clause 37: A system for automatically adjusting insulin therapy for a patient, comprising: one or more memories comprising executable instructions; and one or more processors in data communication with the one or more memories and configured to execute the executable instructions to execute a process for automatically adjusting insulin therapy for the patient, the process comprising: receiving glucose data from a continuous glucose monitor (CGM) sensor indicative of a glucose level of the patient over a time period; determining basal insulin data and bolus insulin data of the patient for the time period; determining a relationship between the basal insulin data and the bolus insulin data; and generating an insulin therapy instruction for the patient based on the glucose data and the determined relationship between the basal insulin data and the bolus insulin data, wherein the insulin therapy instruction comprises a total bolus amount.

[0179] Clause 38: The system of Clause 37, wherein the process for automatically adjusting insulin therapy executes at least hourly.

[0180] Clause 39: The system of Clause 37, wherein the one or more processors are further configured to execute the executable instructions to generate insulin therapy settings based on weighted insulin utilization data for the patient over a plurality of time periods, wherein the insulin therapy instruction is further based on the insulin therapy settings.

[0181] Clause 40: The system of Clause 39, wherein the generation of insulin therapy settings comprises: determining glucose data of the patient for the plurality of time periods; determining glucose control of the patient for the plurality of time periods; determining weighted insulin utilization data for the patient across the plurality of time periods, the weighted insulin utilizationdata comprising weighted basal insulin utilization data and weighted bolus insulin utilization data; determining one or more relationships between the weighted basal insulin utilization data and the weighted bolus insulin utilization data; and generating treatment settings for the patient based on the glucose control, the weighted insulin utilization data, and the determined one or more relationships, the insulin therapy settings comprising the treatment settings.

[0182] Clause 41 : The system of Clause 40, wherein: the determination of the glucose control of the patient comprises computing at least one time in range; and the generation of the treatment settings is based on a determination that the at least one time in range is less than a threshold value.

[0183] Clause 42: The system of Clause 40, wherein: the determination of the glucose control of the patient comprises computing at least one average glucose; and the generation of the treatment settings is based on a determination that the at least one average glucose is greater than a threshold value.

[0184] Clause 43: The system of Clause 42, wherein the treatment settings comprise a target basal / bolus ratio and a basal rate.

[0185] Clause 44: The system of Clause 43, wherein the generating treatment settings comprises adjusting the target basal / bolus ratio and the basal rate.

[0186] Clause 45: The system of Clause 43, wherein: the generation of the insulin therapy instruction comprises determining the total bolus amount based on the target basal / bolus ratio and the basal rate; and the process further comprises commanding an insulin delivery device based on the determined total bolus amount.

[0187] Clause 46: The system of Clause 43, wherein the generation of the insulin therapy instruction comprises: determining a first bolus amount based on a correction factor for the patient; determining a second bolus amount based on the target basal / bolus ratio and the basal rate; and determining the total bolus amount based on the first bolus amount and the second bolus amount.

[0188] Clause 47: The system of Clause 46, wherein the determination of the total bolus amount comprises averaging the first bolus amount and the second bolus amount or summing the first bolus amount and the second bolus amount.

[0189] Clause 48: The system of Clause 46, wherein the determination of the second bolus amount comprises calculating the second bolus amount based on a cumulative amount of basalinsulin administered for the time period, a cumulative amount of bolus insulin administered for the time period, the basal rate, and the target basal / bolus ratio.

[0190] Clause 49: The system of Clause 46, wherein the second bolus amount is non-zero responsive to determinations by the one or more processors that: the glucose data indicates that a current glucose level is in excess of a threshold; and a cumulative insulin administered during the time period is less than a cumulative insulin indicated in the insulin therapy settings for a corresponding period.

[0191] Clause 50: The system of Clause 46, wherein the second bolus amount is non-zero responsive to determinations by the one or more processors that: the glucose data indicates a current glucose level in excess of a threshold; a cumulative total basal insulin for the time period is less than an amount indicated by the basal rate; and a cumulative total bolus insulin for the time period is less than a calculated cumulative total bolus insulin for the time period, wherein the calculated cumulative total bolus insulin for the time period is calculated according to the cumulative total basal insulin and the target basal / bolus ratio.

[0192] Clause 51: A method of automatically adjusting insulin therapy for a patient, comprising: receiving, at a device associated with the patient, one or more glucose measurements generated by a continuous glucose monitoring (CGM) sensor of the patient for a time period; determining, at the device, basal insulin data and bolus insulin data of the patient for the time period; determining, at the device, a relationship between the basal insulin data and the bolus insulin data; and generating, at the device, an insulin therapy instruction for the patient based on the one or more glucose measurements and the determined relationship between the basal insulin data and the bolus insulin data, wherein the insulin therapy instruction comprises a total bolus amount.

[0193] Clause 52: An apparatus, comprising: at least one memory comprising executable instructions; and at least one processor configured to execute the executable instructions and cause the apparatus to perform a method in accordance with any combination of Clauses 19-35 and 51.

[0194] Clause 53: An apparatus, comprising means for performing a method in accordance with any combination of Clauses 19-35 and 51.

[0195] Clause 54: A n on-transitory computer-readable medium comprising executable instructions that, when executed by at least one processor of an apparatus, cause the apparatus to perform a method in accordance with any combination of Clauses 19-35 and 51.

[0196] Clause 55: A computer program product embodied on a computer-readable storage medium comprising code for performing a method in accordance with any combination of Clauses 19-35 and 51.Additional Considerations

[0197] Each of these non-limiting examples can stand on its own or can be combined in various permutations or combinations with one or more of the other examples.

[0198] The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the invention can be practiced. These embodiments are also referred to herein as “examples.” Such examples can include elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. Moreover, the present inventors also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.

[0199] In the event of inconsistent usages between this document and any documents so incorporated by reference, the usage in this document controls.

[0200] In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In this document, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, composition, formulation, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,”“second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.

[0201] Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, in an example, the code can be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

[0202] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is provided to comply with 37 C.F.R. §1.72(b), to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that such embodiments can be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

CLAIMS1. A system for automatically adjusting insulin therapy for a patient, comprising: one or more memories comprising executable instructions; andone or more processors in data communication with the one or more memories and configured to execute the executable instructions to execute a process for automatically adjusting insulin therapy for the patient, the process comprising:receiving glucose data from a continuous glucose monitoring (CGM) sensor indicative of a glucose level of the patient over a time period;determining basal insulin data and bolus insulin data of the patient for the time period;determining a relationship between the basal insulin data and the bolus insulin data; andgenerating an insulin therapy instruction for the patient based on the glucose data and the determined relationship between the basal insulin data and the bolus insulin data, wherein the insulin therapy instruction comprises a total bolus amount.

2. The system of claim 1, wherein the process for automatically adjusting insulin therapy executes at least hourly.

3. The system of claim 1, wherein the one or more processors are further configured to execute the executable instructions to generate insulin therapy settings based on weighted insulin utilization data for the patient over a plurality of time periods, wherein the insulin therapy instruction is further based on the insulin therapy settings.

4. The system of claim 3, wherein the generation of insulin therapy settings comprises:determining glucose data of the patient for the plurality of time periods; determining glucose control of the patient for the plurality of time periods; determining weighted insulin utilization data for the patient across the plurality of time periods, the weighted insulin utilization data comprising weighted basal insulin utilization data and weighted bolus insulin utilization data;determining one or more relationships between the weighted basal insulin utilization data and the weighted bolus insulin utilization data; andgenerating treatment settings for the patient based on the glucose control, the weighted insulin utilization data, and the determined one or more relationships, the insulin therapy settings comprising the treatment settings.

5. The system of claim 4, wherein:the determination of the glucose control of the patient comprises computing at least one time in range; andthe generation of the treatment settings is based on a determination that the at least one time in range is less than a threshold value.

6. The system of claim 4, wherein:the determination of the glucose control of the patient comprises computing at least one average glucose; andthe generation of the treatment settings is based on a determination that the at least one average glucose is greater than a threshold value.

7. The system of claim 6, wherein the treatment settings comprise a target basal / bolus ratio and a basal rate.

8. The system of claim 7, wherein the generating treatment settings comprises adjusting the target basal / bolus ratio and the basal rate.

9. The system of claim 7, wherein:the generation of the insulin therapy instruction comprises determining the total bolus amount based on the target basal / bolus ratio and the basal rate; andthe process further comprises commanding an insulin delivery device based on the determined total bolus amount.

10. The system of claim 7, wherein the generation of the insulin therapy instruction comprises:determining a first bolus amount based on a correction factor for the patient; determining a second bolus amount based on the target basal / bolus ratio and the basal rate; anddetermining the total bolus amount based on the first bolus amount and the second bolus amount.

11. The system of claim 10, wherein the determination of the total bolus amount comprises averaging the first bolus amount and the second bolus amount or summing the first bolus amount and the second bolus amount.

12. The system of claim 10, wherein the determination of the second bolus amount comprises calculating the second bolus amount based on a cumulative amount of basal insulin administered for the time period, a cumulative amount of bolus insulin administered for the time period, the basal rate, and the target basal / bolus ratio.

13. The system of claim 10, wherein the second bolus amount is non-zero responsive to determinations by the one or more processors that:the glucose data indicates that a current glucose level is in excess of a threshold; and a cumulative insulin administered during the time period is less than a cumulative insulin indicated in the insulin therapy settings for a corresponding period.

14. The system of claim 10, wherein the second bolus amount is non-zero responsive to determinations by the one or more processors that:the glucose data indicates a current glucose level in excess of a threshold;a cumulative total basal insulin for the time period is less than an amount indicated by the basal rate; anda cumulative total bolus insulin for the time period is less than a calculated cumulative total bolus insulin for the time period, wherein the calculated cumulative total bolus insulin for the time period is calculated according to the cumulative total basal insulin and the target basal / bolus ratio.

15. A method of automatically adjusting insulin therapy for a patient, comprising: receiving, at a device associated with the patient, one or more glucose measurements generated by a continuous glucose monitoring (CGM) sensor of the patient for a time period; determining, at the device, basal insulin data and bolus insulin data of the patient for the time period;determining, at the device, a relationship between the basal insulin data and the bolus insulin data; andgenerating, at the device, an insulin therapy instruction for the patient based on the one or more glucose measurements and the determined relationship between the basal insulin data and the bolus insulin data, wherein the insulin therapy instruction comprises a total bolus amount.

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