ANTIHYPERGLYCEMIC METHODS AND SYSTEMS FIELD [0001] The subject matter of this disclosure generally relates to systems, devices, and methods relating to glucose disorders. More specifically, this disclosure relates to analyzing a patient’s glucose data to determine if initiation or adjustment of one or more glucose disorder treatments is warranted. BACKGROUND [0002] This disclosure relates to the field of treatment of glucose disorders in human patients. Specifically, this disclosure is related to systems and methods for improving detection of glucose disorders and initiating or escalating therapy for the same. [0003] Glucose disorders occur when a patient’s blood glucose levels drop below or rises above the normal blood glucose range. There are several known therapies that can be initiated to address these types of disorders. Typically, treatment is initiated in consultation with a health care professional after a patient presents with symptoms. A blood glucose reading is usually taken to confirm the patient’s blood glucose level prior to treatment. [0004] The introduction of personal continuous glucose monitors (“CGM”) has increased the frequency of glucose data available to a patient and their health care professional. Clinical studies in hospital settings have shown that computerized algorithms can improve patient outcomes by allowing a tighter control over blood glucose levels by analyzing data from hospital-based CGMs. This disclosure details systems and methods of using data received from a CGM to analyze the blood glucose levels of a patient and determine various metrics for improving treatment initiation. BRIEF SUMMARY OF THE INVENTION [0005] In a first embodiment, a method of determining an optimized dosage regimen for a glucose disorder includes receiving existing dosage information for a patient at a reader device; receiving, by the computing device, glucose data from a patient using a continuous glucose monitor comprising a first portion arranged above a skin surface of the patient and a second portion arranged below the skin surface and in contact with
interstitial fluid of the patient; determining glycemic risks for a plurality of time periods based on the received glucose data; modifying an existing basal insulin dosage if the existing basal insulin dosage is below a predetermined maximum amount, and if the glycemic risks indicate an elevated risk of a high glucose level in the time period and the glycemic risks do not indicate an elevated risk of low glucose in the time period; initiating a prandial insulin dose if the existing basal insulin dosage is at a predetermined maximum amount and the glycemic risks indicate an elevated risk of a high glucose level in the time period; and outputting dosage instructions comprising a modification to the existing basal insulin dosage or an initiation of the prandial insulin dose to a second reader device using a communication system. [0006] In a further embodiment, the glycemic risks include a likelihood of low glucose determined by taking a sum of the differences between a plurality of glucose measurements and a predetermined glucose value and dividing the sum by a total number of the plurality of glucose measurements. [0007] In a further embodiment, the elevated risk of a high glucose level is determined by calculating a median glucose for the time period and comparing the median glucose to at least one predetermined glucose value. [0008] In a further embodiment, the time period is one of a plurality of time periods, the method further comprising separating the glucose data into data sets based on the plurality of time periods, respectively, wherein the processing, comparing, determining and outputting steps are performed on each of the two data sets separately. [0009] In a further embodiment, each time period is associated with an event that occurs during the time period. [0010] In a further embodiment, the event is a meal. [0011] In a further embodiment, the method includes determining if the existing dosage information includes a prandial insulin treatment; and adjusting the existing prandial insulin treatment by increasing the prandial insulin treatment when the glycemic risks indicate an elevated risk of a high glucose level and when the existing basal insulin dosage is at a predetermined maximum. [0012] In a further embodiment, the method includes determining if the glycemic risks indicate an elevated risk of a high glucose level and when the existing basal insulin dosage is at a predetermined maximum in more than one time period; and recommending a prandial medication dose for the first of the one or more time periods when the
glycemic risks indicate an elevated risk of a high glucose level and when the existing basal insulin dosage is at a predetermined maximum in more than one time period. [0013] In a further embodiment, the method includes determining if the glycemic risks indicate an elevated risk of a high glucose level and when the existing basal insulin dosage is at a predetermined maximum in more than one time period; and recommending a prandial medication dose for the time period with the highest glucose median. [0014] In an embodiment, system for determining a dosage regimen for a glucose disorder includes a continuous glucose monitor, comprising: a glucose sensor comprising a first portion configured to be arranged above the skin and a second portion configured to be arranged below the skin and in contact with interstitial fluid to sense glucose within the interstitial fluid; and sensor electronics coupled to the glucose sensor and comprising a memory, communication circuitry, and processor coupled to the memory and communication circuitry. The system also includes a computing device including a processor and memory, the computing device operably linked the continuous glucose monitor, wherein the memory stores instructions that when executed by the processor cause the processor to: receive the glucose data from the continuous glucose monitor; retrieve existing dosage information from the memory; determine glycemic risks for a plurality of time periods based on the received glucose data; modify an existing basal insulin dosage if the existing basal insulin dosage is below a predetermined maximum amount, and if the glycemic risks indicate an elevated risk of a high glucose level in the time period and the glycemic risks do not indicate an elevated risk of low glucose in the time period; initiate a prandial insulin dose if the existing basal insulin dosage is at a predetermined maximum amount and the glycemic risks indicate an elevated risk of a high glucose level in the time period; and output instructions comprising a modification to the existing basal insulin dosage or an initiation of the prandial insulin dose to the computing device. [0015] In a further embodiment, the memory further comprises instructions that instruct the processor to calculate risk of low glucose by taking a sum of the differences between a plurality of glucose measurements and a predetermined glucose value and dividing the sum by a total number of the plurality of glucose measurements. [0016] In a further embodiment, the memory further comprises instructions that instruct the processor to determine the elevated risk of high glucose by calculating the median glucose and comparing the median glucose to a predetermined value to determine a risk.
[0017] In a further embodiment, the time period is one of a plurality of time periods, and wherein the instructions further cause the processor to separate the glucose data into data sets based on the plurality of time periods, respectively, wherein the processing, comparing, determining and outputting steps are performed on each of the two data sets separately. [0018] In a further embodiment, each time period is associated with an event that occurs during the time period. [0019] In a further embodiment, the instructions further cause the processor to determine if the glycemic risks indicate an elevated risk of a high glucose level and when the existing basal insulin dosage is at a predetermined maximum in more than one time period; and recommend a prandial medication dose for the first of the one or more time periods when the glycemic risks indicate an elevated risk of a high glucose level and when the existing basal insulin dosage is at a predetermined maximum in more than one time period. [0020] In a further embodiment, the instructions further cause the processor to determine if the existing dosage information includes a prandial insulin treatment; adjust the existing prandial insulin treatment by increasing the prandial insulin treatment when the glycemic risks indicate an elevated risk of a high glucose level and when the existing basal insulin dosage is at a predetermined maximum. [0021] In a further embodiment, the computing device comprises a display that is configured to display the dosage instructions. [0022] In a further embodiment, in the method of any one of the above method embodiments the determining if the glucose data indicates an overbasalization; and updating the output instructions to recommend a reduction in basal insulin. [0023] In a further embodiment, in the method of any one of the above method embodiments, determining if the glucose data indicates an overbasalization comprises comparing the difference between a maximum and a minimum glucose in an overnight time period to a predetermined difference amount, and determining that overbasalization is occurring if the difference exceeds the predetermined difference level. [0024] In a further embodiment, in the method of any one of the above method embodiments, determining if the glucose data indicates an overbasalization comprises calculating the slope of a line fit to the decrease in glucose in an overnight time period,
and comparing the slope to a predetermined slope, wherein determining that overbasalization is occurring if the slope exceeds the predetermined slope. [0025] In a further embodiment, in the method of any one of the above method embodiments, determining if the glucose data indicates overbasalization includes using one of a running average or weighted average of glucose data taken from multiple days. [0026] In a further embodiment of any one of the system claims above, the instructions further cause the processor to determine if the glucose data indicates an overbasalization; and update the output instructions to recommend a reduction in basal insulin. [0027] In a further embodiment of any one of the system embodiments above, determining if the glucose data indicates an overbasalization comprises comparing the difference between a maximum and a minimum glucose in an overnight time period to a predetermined difference amount, and determining that overbasalization is occurring if the difference exceeds the predetermined difference level. [0028] In a further embodiment of any one of the system embodiments above, determining if the glucose data indicates an overbasalization comprises calculating the slope of a line fit to the decrease in glucose in an overnight time period, and comparing the slope to a predetermined slope, wherein determining that overbasalization is occurring if the slope exceeds the predetermined slope. [0029] In a further embodiment of any one of the system embodiments above, determining if the glucose data indicates overbasalization includes using one of a running average or weighted average of glucose data taken from multiple days. [0030] In a further embodiment of any one of the method embodiments above the method includes receiving the patient’s existing medications; and adding the existing medications to the output instructions; and updating the existing medications in the output instructions to accommodate a modification to an existing dosage. [0031] In a further embodiment of any one of system embodiments above, the instructions further cause the processor to: receive the patient’s existing medications; and add the existing medications to the output instructions; and update the existing medications in the output instructions to accommodate a modification to an existing dosage. [0032] A method of determining a dosage regimen for a glucose disorder includes receiving existing dosage information for a patient at a computing device and receiving, by the computing device, glucose data from a patient using a continuous glucose monitor.
Next, the method includes determining a glycemic risk for a time period based on the glucose data, and modifying an existing basal insulin dosage when the glycemic risk indicates an elevated risk of a high glucose level in at least one time period and when the glycemic risk does not indicate an elevated risk of low glucose, and when the existing basal insulin dosage is below a predetermined maximum. Next, the method includes initiating a prandial insulin dose when the glycemic risk indicates an elevated risk of a high glucose level and when the existing basal insulin dosage is at a predetermined maximum. Certain aspects of the disclosure have other steps or elements in addition to or in place of those mentioned above. The steps or elements will become apparent to those skilled in the art from a reading of the following detailed description when taken with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES [0033] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate the present disclosure and, together with the description, further serve to explain the principles thereof and to enable a person skilled in the pertinent art to make and use the same. [0034] FIG.1 is a system overview of a sensor applicator, reader device, monitoring system, network, and remote system according to an embodiment. [0035] FIG.2A is a block diagram depicting an example embodiment of a reader device according to an embodiment. [0036] FIGS.2B and 2C are block diagrams depicting example embodiments of sensor control devices according to embodiments. [0037] FIG.3 is a perspective view of a sensor according to an embodiment. [0038] FIG.4 is a process flow chart of a method of analyzing glucose data according to an embodiment. [0039] FIG.5A is a graph of glucose data analyzed by the dosage regimen determination system according to an embodiment. [0040] FIG.5B is a graph of glucose data analyzed by the dosage regimen determination system according to an embodiment. [0041] FIG.6 is a process flow chart of a method of determining dosage regimen initiation according to an embodiment
[0042] FIG.7 is a process flow chart of a method of determining dosage regimen initiation according to an embodiment [0043] FIG.8 is a process flow chart of a method of determining dosage regimen initiation according to an embodiment [0044] FIG.9 is a block diagram of system including a medication delivery device 108 according to an embodiment. [0045] In the drawings, like reference numbers generally indicate identical or similar elements. Additionally, generally, the left-most digit(s) of a reference number identifies the drawing in which the reference number first appears. DETAILED DESCRIPTION [0046] Reference will now be made in detail to representative embodiments illustrated in the accompanying drawings. References to “one embodiment,” “an embodiment,” “an exemplary embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such a feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. [0047] Glucose levels in the human body fluctuate throughout the day based on several different factors. One of the most significant factors is food intake, which results in a post-meal rise in blood glucose levels. Conversely, fasting usually results in a drop in blood glucose levels. This variability is not constant across patients. For example, different patients can have different variabilities, and can have blood glucose levels that react to meals and fasting differently. This means that a single glucose measurement taken at a health care professional’s office may not be indicative of a patient’s typical blood glucose levels, which can result in non-optimal therapy. When considering initiation of a dosage regimen or therapy, this could result in premature initiation of therapy (based on an atypical rise in blood glucose levels) or delayed initiation (based on an atypical drop in blood glucose levels).
[0048] A fully escalated multiple-daily-injection (MDI) therapy regimen includes one or more daily doses of basal (long-acting) insulin, and a rapid-acting insulin bolus dose for one or more typical meals (e.g., breakfast, lunch and/or dinner). A patient can be started on basal-only therapy and over time, as needed to control the patient’s glucose levels, meal doses can be introduced one at a time. For instance, a patient on basal-only therapy may start taking a dinner-time dose if they are not achieving their glycemic or A1c goals. Health care professionals prescribe the initial meal dose for the meal that patients indicate is the largest. However, patients may not correctly identify the meal with the largest amount of carbohydrates (or that results in the largest glycemic impact). [0049] This problem is more acute when the patient has already started one therapy and is considering initiation of another therapy. An example is the initiation of mealtime or prandial insulin in a patient that is already using slow-acting or basal insulin. Prandial insulin is a dose of insulin that is given at or around mealtimes and generally uses rapidly- acting insulin. Health care professionals gradually increase basal insulin, which is usually taken once or twice a day, until peak blood sugar exceeds a desired limit or the dosage of basal insulin reaches a predetermined maximum. Then, the health care professional will instruct the patient to begin taking prandial insulin after a meal. Because of the variability of blood glucose readings and the lack of data regarding the patient’s specific blood glucose levels, this can result in a delayed introduction of prandial insulin, a basal insulin dose that is too high, or non-optimal timing and dosing of prandial insulin. Thus, there is a need to improve the identification and initiation of glucose therapies. [0050] FIG.1 is a conceptual diagram depicting an example embodiment of a continuous glucose monitor 100 that includes a sensor applicator 150, a sensor control device 102, and a reader device 120. Here, sensor applicator 150 can be used to deliver sensor control device 102 to a monitoring location on a user's skin where a sensor 104 is maintained in position for a period of time by an adhesive patch 105. Sensor control device 102 is further described in FIGS.2B and 2C, and can communicate with reader device 120 via a communication path 140 using a wired or wireless technique. Example wireless protocols include Bluetooth, Bluetooth Low Energy (BLE, BTLE, Bluetooth SMART, etc.), Near Field Communication (NFC) and others. Users can monitor applications installed in memory on reader device 120 using display 122 and input 121, and the device battery can be recharged using power port 123. While only one reader device 120 is shown, sensor control device 102 can communicate with multiple reader devices 120. Each of the reader
devices 120 can communicate and share data with one another. More details about reader device 120 are set forth with respect to FIG.2A below. Reader device 120 can communicate with local computer system 170 via a communication path 141 using a wired or wireless communication protocol. Local computer system 170 can include one or more of a laptop, desktop, tablet, phablet, smartphone, set-top box, video game console, or other computing device and wireless communication can include any of a number of applicable wireless networking protocols including Bluetooth, Bluetooth Low Energy (BTLE), Wi-Fi or others. Local computer system 170 can communicate via communications path 143 with a network 190 similar to how reader device 120 can communicate via a communications path 142 with network 190, by a wired or wireless communication protocol as described previously. Network 190 can be any of a number of networks, such as private networks and public networks, local area or wide area networks, and so forth. A trusted computer system 180 can include a server and can provide authentication services and secured data storage and can communicate via communications path 144 with network 190 by wired or wireless technique. [0051] FIG.2A is a block diagram depicting an example embodiment of a reader device 120 configured as a smartphone. Here, reader device 120 can include a display 122, input component 121, and a processing core 306 including a communications processor 322 coupled with memory 323 and an applications processor 324 coupled with memory 325. Also included can be separate memory 330, RF transceiver 328 with antenna 329, and power supply 326 with power management module 338. Further, reader device 120 can also include a multi-functional transceiver 332 which can communicate over Wi-Fi, NFC, Bluetooth, BTLE, and GPS with an antenna 334. As understood by one of skill in the art, these components are electrically and communicatively coupled in a manner to make a functional device. [0052] FIGS.2B and 2C are block diagrams depicting example embodiments of sensor control devices 102 having analyte sensors 104 and sensor electronics 160 (including analyte monitoring circuitry) that can have the majority of the processing capability for rendering end-result data suitable for display to the user. In FIG.2B, a single semiconductor chip 161 is depicted that can be a custom application specific integrated circuit (ASIC). Shown within ASIC 161 are certain high-level functional units, including an analog front end (AFE) 162, power management (or control) circuitry 164, processor 166, and communication circuitry 168 (which can be implemented as a
transmitter, receiver, transceiver, passive circuit, or otherwise according to the communication protocol). In this embodiment, both AFE 162 and processor 166 are used as analyte monitoring circuitry, but in other embodiments either circuit can perform the analyte monitoring function. Processor 166 can include one or more processors, microprocessors, controllers, and/or microcontrollers, each of which can be a discrete chip or distributed amongst (and a portion of) a number of different chips. [0053] A memory 163 is also included within ASIC 161 and can be shared by the various functional units present within ASIC 161, or can be distributed amongst two or more of them. Memory 163 can also be a separate chip. Memory 163 can be volatile and/or non- volatile memory. In this embodiment, ASIC 161 is coupled with power source 170, which can be a coin cell battery, or the like. AFE 162 interfaces with in vivo analyte sensor 104 and receives measurement data therefrom and outputs the data to processor 166 in digital form, which in turn processes the data to arrive at the end-result glucose discrete and trend values, including the algorithms described in detail below. This data can then be provided to communication circuitry 168 for sending, by way of antenna 171, to reader device 120 (not shown), for example, where minimal further processing is needed by the resident software application to display the data. [0054] FIG.2C is similar to FIG.2B but instead includes two discrete semiconductor chips 162 and 174, which can be packaged together or separately. Here, AFE 162 is resident on ASIC 161. Processor 166 is integrated with power management circuitry 164 and communication circuitry 168 on chip 174. AFE 162 includes memory 163 and chip 174 includes memory 165, which can be isolated or distributed within. In one example embodiment, AFE 162 is combined with power management circuitry 164 and processor 166 on one chip, while communication circuitry 168 is on a separate chip. In another example embodiment, both AFE 162 and communication circuitry 168 are on one chip, and processor 166 and power management circuitry 164 are on another chip. It should be noted that other chip combinations are possible, including three or more chips, each bearing responsibility for the separate functions described, or sharing one or more functions for fail-safe redundancy. [0055] FIG.3 is a perspective view depicting an example embodiment of sensor 104. Sensor 104 is a transcutaneous sensor having an in-vivo portion 1401 and an ex-vivo portion 1402. In-vivo portion 1401 is the portion that is inserted into the patient. For example, in-vivo portion 1401 may be inserted into the skin of the patient and be placed
into contact with interstitial fluid. Ex-vivo portion 1402 generally remains outside of the patient and is the portion of sensor 104 that mechanically and electrically interfaces with other elements, such as sensor electronics 160. A neck 1406 can be a zone which allows folding of the sensor, for example ninety degrees. A membrane on tail 1408 can cover an active analyte sensing element of the sensor 104. Tail 1408 can be the portion of sensor 104 that resides under a user's skin after insertion. A flag 1404 can contain contacts and a sealing surface. A biasing tower 1412 can be a tab that biases the tail 1408 for mechanical connection. A bias fulcrum 1414 can be an offshoot of biasing tower 1412 that contacts an inner surface of a needle to bias a tail into a slot. A bias adjuster 1416 can reduce a localized bending of a tail connection and prevent sensor trace damage. Contacts 1418 can electrically couple the active portion of sensor 104 to suitable contacts for electrical connection to sensor electronics 160. A service loop 1420 can translate an electrical path from a vertical direction ninety degrees to flag 1404. [0056] The components discussed above provide data monitoring and processing capabilities that can be used to improve the initiation and adjustment of therapies for treating any detected glucose disorders. The algorithms discussed below use the data and processing capability to produce treatment therapies or treatment therapy adjustments tailored to the relevant patient. The calculations below comprise two subordinate algorithms. A data processing algorithm 210 processes the raw data taken by continuous glucose monitor 100. A dosage regimen or therapy algorithm 220 takes the output from data processing algorithm 210 to provide dosage regimen recommendations or instructions. Therapy algorithm 210 is configured to run on any suitable processing device or devices, and may be stored by any suitable connected memory, including in one or more of sensor control device 102, reader device 120, and a remote computer or server (e.g., cloud). Any of these devices may be networked together such that therapy algorithm 210 can be run by a combination of these devices. [0057] In some embodiments, therapy algorithm 220, as shown, for example, in FIG.6, describes a process where recommendations are made to escalate therapy by adding new medication doses (for instance, starting the patient, who is currently on basal-only therapy, on a rapid-acting dose for a meal), adjust dose amounts (e.g., increase and/or decrease amount), recommend new therapies, among others. The method described below utilizes the outputs of data processing algorithm 210, which will be described below. This analysis identifies patterns for various time periods. The time periods may include one or
more time periods in a day. Some examples of time periods can include post-breakfast, post-lunch, post-dinner, post-snack and/or overnight (e.g., fasting) periods. The time periods may be fixed, or may be customized to the particular patient based on glucose, insulin, sleep pattern, meal intake, and/or activity data. Patterns may be determined using continuous glucose data specific to time-of-day periods; for instance, the post-breakfast pattern may be determined by glucose values that occur between 8am and 12pm. Or these patterns may be determined by using CGM data aligned to insulin doses; for instance, the post-breakfast pattern may be determined by glucose values that occur during the 3, 5, or any predetermined period following a recorded insulin dose taken for breakfast (e.g., a morning meal bolus). In some examples, the post-breakfast period may be split into post- breakfast and pre-lunch (or pre-snack) periods. Similarly, the post-lunch period can be split into post-lunch and pre-dinner (or pre-snack) periods. The pre- and post-meal period durations may be predetermined (e.g., 3-hour post-meal and 2 hour pre-meal) or determined dynamically based on glucose data (e.g., the post meal period will include the duration that the user’s glucose levels fall to normal glucose level, which will mark the beginning of the next pre-meal period) or insulin data [0058] Generally, these patterns may be determined by any recorded marker in time (such as a meal record or activity record) which may be associated with a subsequent glycemic response. The patterns identified include, for example: High Glucose pattern (high glucose levels with low risk of hypoglycemia), Low Glucose pattern (high risk of hypoglycemia), High/Low Glucose pattern (high glucose levels with moderate risk of hypoglycemia), In-target Glucose pattern (low risk of hypoglycemia), Moderate/Low Risk pattern (In-target glucose levels with moderate risk of hypoglycemia) and/or No pattern (e.g., data not sufficient to identify a pattern). Variations of these patterns or other patterns that are correlated to a medical condition of interest can also be identified by data processing algorithm 210. Data processing algorithm 210 is configured to run on any suitable processing device or devices, and may be stored by any suitable connected memory, including in one or more of sensor control device 102, reader device 120, and a remote computer or server (e.g., cloud). Any of these devices may be networked together such that data processing algorithm 210 can be run by a combination of these devices. [0059] Data processing algorithm 210, as shown in FIG.4, begins by defining data sets that span the data received in specific time periods 212 for analysis. Time periods 212 can be any predetermined length of time. Time periods 212 can be determined such that a set
of time periods 212 is equal to one full day. A patient’s glucose levels typically vary in similar ways each day because of the patient’s daily routine. Thus, setting time periods 212 to span the same time during each day improves the quality of the data analysis done when combining multiple time periods. FIG.5A is a representative graph of the monitored glucose levels of a patient during a day. As seen in FIG.5A, five time periods 212 are present. Other examples of suitable time periods 212 can include four six-hour time periods, six four-hour time periods 212, or eight three-hour time periods 212. Time periods 212 may also be set based on events that occur during a typical day. Time periods 212 can be set by data processing algorithm 210, or can be input by a user (e.g., the HCP or patient). For example, time period 212 can be based around recurring events such as meals and sleep, which can be inputs that are changed based on the patient’s information. In an embodiment, the time periods 212 can include four time periods, with one time period 212 corresponding to the sleep or overnight time frame, and the other three time periods 212 including one of breakfast, lunch, or dinner, and the timespan between that meal and the start of the next time period 212 (e.g., the pre- and/or post-meal periods described above). [0060] Data 214 is collected from continuous glucose monitor 100 during each time period 212 and is processed to produce various glycemic risks of the data. One or more of these glycemic risks or metrics may be determined based on data from continuous glucose monitor 100. In some embodiments, four glycemic risks or metrics are determined. First, a median glucose level 215 is calculated, and this can be compared directly to a target glucose goal. Second, a hypoglycemia risk metric, such as a likelihood of low glucose (“LLG”) 217 is calculated, which is a mathematical representation of the likelihood of an excessively low glucose occurring. Third, a measure of glucose variability 218 is calculated. Fourth, the total number of data points that exceed preset high and low glucose limits are also recorded. [0061] Median 215 can be used as a direct comparison to a target glucose level or median goal 216 for a patient, as will be discussed in detail below. The difference between median 215 and median goal 216 can be assessed against a series of predetermined values that categorize how the measured median 215 performs versus medial goal 216. The user’s glucose median may be used to assess a risk level from a predetermined list risk levels. The risk levels may include Low, Medium or High risk levels. However, in some embodiments, fewer or additional ratings maybe included, e.g., Very High, among others.
For example, there may be two different predetermined values, with corresponding ratings of Low (difference below the first value), Medium (difference between the first value and second value), and High (difference above the second value). These values correspond to a risk of the patient’s typical glucose reading becoming excessively high, with low, medium, and high indicating increasing risks levels. Median 215 may also be calculated in different ways that achieve the same function of comparing the target glucose level to a value that represents the typical glucose level of the patient. In some embodiments, other measures of central tendency may be used, such as an average glucose or mean glucose, among others. [0062] In some embodiments, LLG 217 is calculated by taking the difference between each glucose reading that is below a predetermined value and the predetermined value, summing those differences, and dividing by the total number of measurements. This value is then compared to a predetermined threshold. The threshold may be defined by a Low Glucose Allowance parameter. For example, if the predetermined parameter is 70 mg/dL, and a reading is x1, then the sum is taken for each difference determined by the formula 70mg/dL-x1 mg/dL. The Low Glucose Allowance parameter is set to allow a certain amount of excursions below the predetermined limit, and the nature of the summation of the differences of the low readings account for both the frequency of the excursions and magnitude. For example, five low excursions of only 5mg/dL below the limit would result in a value of 5 mg/dL, while two excursions of 15 mg/dL would add up to a higher number (7.5 mg/dL). The precise value of the Low Glucose Allowance can be varied based on a risk tolerance of the algorithm. There can also be a range of values for Low Glucose Allowance that can result in various ratings. For example, there may be three different increasing values, with corresponding ratings of None (below the first value), Low (between the first value and second value), and Medium (between the second and third value) and High (above the third value). The values may be tailored specifically to the patient, by taking into account details related to the patient’s physical condition. In this way, for example, a lower risk tolerance (and thus, a lower Low Glucose Allowance) can be assigned to patients at higher risk of health complications, and vice versa. LLG 217 may also be calculated in different ways that achieve the same function of presenting a risk of a low glucose level. [0063] In some embodiments, glucose variability 218 is calculated as the difference between the glucose values that fall in the lower tenth percentile of all readings and
glucose median 215. These differences can be added and compared to a predetermined variability in a similar fashion as discussed above with respect to LLG 217. The total number of excursions, either high or low, can also be compared to predetermined values in a similar manner as discussed above with respect to glucose median 215. For example, there may be two different predetermined values, with corresponding ratings of Low (excursions below the first value), Medium or Moderate (excursions between the first value and second value), and High (excursions above the second value). However, in some embodiments, fewer or additional ratings maybe included, e.g., Very High, among others. Variability 218 may also be calculated in different ways that achieve the same function of presenting a risk of a high variability in typical glucose level of the patient. [0064] Data processing algorithm 210 is intended to run continuously and to collect data over several time periods, for example over multiple days (e.g., 2, 3, 7, 14, 15, 30, or any number of days). When this occurs, data processing algorithm 210 is configured to store the results discussed above on suitable memory in one or more of sensor control device 102, reader device 120, and a remote computer or server (e.g., cloud). The different analysis variables discussed above can be aggregated together across multiple days to improve the accuracy of data processing algorithm 210. This is especially relevant for patients who generally follow a similar schedule across different days. For example, weekdays may be analyzed separately from weekend days. [0065] FIG.5A shows a sample graph of hypothetical glucose levels of a patient over a single day. Different time periods 212 are indicated by variables t1-t5, and as can be seen here starting at 3am. They are variable in length, with t1, t2, and t5 being five hours, t3 being six hours, and t4 being three hours. Median 215 is visible, as are various percentiles, median goal 216, and the low glucose threshold (here, 70 mg/dL). A table below the graph has scores identifying how each of the measures discussed above (median 215, LLG 217, and variability 218) for each time period is scored as per the disclosure discussed above. In some embodiments, a graph of glucose levels over one or more days may be output to the user. In some embodiments, ratings for the various parameters may be output to the user. In some embodiments, the identified patterns may be output to the user. [0066] In some embodiments, data processing algorithm 210 can also address issues caused by excessive amounts of medication being administered to a patient. One such issue is generally termed overbasalization, which occurs when too much basal, slow-
acting, or pre-mixed insulin is administered (a pre-mixed combination or slow-acting and rapid-acting insulin). This problem can occur as a result of escalating just one o more therapies—basal insulin mixed insulin, rapid-acting insulin, and/or GLP-1—for example, in response to high glucose during one portion of the day. This often results in excessively low blood glucose levels overnight because (1) basal insulin is usually injected before bedtime; and (2) there is no meal in the overnight period to counteract the effects of the basal insulin. Addressing this issue with single measurements of glucose is difficult because the hypoglycemia usually occurs at night, when measurements are not normally taken. Further, overbasalization may also be present even if the patient’s overnight glucose levels do not drop below a predetermined minimum. [0067] FIG.5B shows another sample data graph of hypothetical glucose levels of a patient over a single day. This graph shows an example of overbasalization. As seen in FIG.5B, this problem is occurring overnight, some time after the final meal has been eaten (typically in the early morning hours). As seen in FIG.5B, the dark bold overbasalization line 213 drops below the low glucose threshold between approximately 4AM and 8AM (in the overnight hours). Data processing algorithm 210 can be used to detect overbasalization, which can then be used as an input to therapy algorithm 220 as described below. The data variables analyzed here can be the same as discussed above, and overbasalization is indicated by a raised LLG 217 that is paired with a Low or Medium risk when comparing median 215 to median goal 216. Data processing algorithm 220 can also determine the number of times when the difference between maximum and minimum overnight readings exceed a predetermined value, assuming the maximum value is recorded before the minimum value. This predetermined value could be between 30 mg/dL and 100 mg/dL. This allows data processing algorithm 210 to determine the number of times that a patient’s glucose drops more than the predetermined amount, which would indicate overbasalization. This metric can be calculated as a moving average or weighted average that takes into account past data and adds weight to more recent data. [0068] In other embodiments, additionally or alternatively data processing algorithm 210 can determine a downward sloping line that fits the overnight drop in glucose data and compare the slope of that line to a predetermined value. If the slope is greater than the predetermined value (the drop is steeper), then that can indicate overbasalization. This metric can also be calculated by a moving or weighted average, as discussed above. The overbasalization techniques discussed here may also be used to provide information to the
end user (the patient or health care professional) and may not affect operation of therapy algorithm 220. [0069] The methods discussed below are based on the premise that health care professionals may wish initiate prandial insulin based on the post-meal period with the highest glucose levels based on the algorithms described herein. This will provide the patient with the most glycemic benefit for the cost of an additional injection. Alternatively, the clinician may initiate the prandial insulin based on addressing one of many High Glucose patterns. For instance, if the patient had a High pattern for breakfast, and a High pattern for dinner, even if the median glucose levels were higher for breakfast, the patient may find it more convenient to initiate the meal dose for dinner and the system may weight a dinner-time bolus recommendation higher than a breakfast-time bolus. Finally, it is important to not initiate a prandial insulin dose when there is a risk of unacceptable hypoglycemia. The pattern analysis provides this information: Low, High/Low, and Moderate Risk patterns indicate this risk. A prandial dose introduced to a meal with a High/Low pattern may result in creating a Low pattern. Also, if a dinner time prandial dose was introduced where there was a Moderate Hypo Risk overnight, the Low pattern may be created for subsequent overnight periods. This disclosure describes a methodology to guide the clinician in making therapy changes that reducing hypoglycemia while managing hypoglycemia. [0070] The method described here provides periodic recommendations to health care professionals for modifying a patient’s dosage regimen or provides periodic recommendations directly to the patient. Some embodiments automatically update the patient’s dose regimen parameters, but provides recommendations regarding adding meal- doses (including what meal to add the dose and the amount) to the clinician. This approach provides the patient and clinician an opportunity to discuss which meal dose to add and for the clinician to educate the patient about the meal-dosing. Identification of these recommendations results in better health outcomes because it provides tailored recommendations for the patient. Algorithms discussed here also automatically improve therapy regimens (without increasing the burden on the HCP), and thus reduce the amount of time the clinician needs to optimize a patient’s therapy. [0071] There are a number of ways to describe dose regimen parameters. The examples given here are intended to be illustrative. Embodiments may include all, some, or none of the factors and treatments described here. In some embodiments, a dose regimen is
defined by one or more of the following parameters: Basal Dose(s), Carbohydrate Ratio (CR), Correction Factor (CF), and target glucose (TG). These parameters are used by the patient (or by a calculator or lookup table used by the patient) to determine how much insulin to dose. Basal Dose may refer to long-acting insulin dose. The CR is used to determine how much rapid-acting insulin to take for each meal; the patient must estimate the carbohydrates they will consume and apply the CR to calculate the dose. The CF and TG are used to determine if the patient should dose more or less insulin based on their current glucose values. Another dose regimen may define parameters corresponding to a dose for each meal, e.g., a breakfast dose, a lunch dose, and/or a dinner dose. This replaces the CR parameter. There are many possible regimens and regimen parameters that may be used. When this disclosure describes optimizing or titrating an insulin regimen, it is referring to modification of these parameters in order to improve glycemic control. For patients who are not on fully escalated MDI therapy, it is useful to consider the latter regimen described above where some of the meal doses are set to zero. Adding a dose would be to modify the regimen where a meal dose is changed from zero to some non-zero value. [0072] In some embodiments, the method described here takes the approach of substantially optimizing the current dosage regimen to achieve the desired glycemic control, and then recommending an additional dose if needed. In some embodiments, glycemic control is defined as maintaining glucose levels substantially within a target range, for example between 70 and 180 mg/dL. A dosage regimen is optimized when no improvement to glycemic control can be made by further adjustments to the regimen parameters. The time that glucose levels are within the predetermined range can also be considered when determining if a treatment is optimized. For example, this time-in-range can be calculated as the percentage of time the patient’s glucose readings are within the desired range. The time-in-range goals can be set as a percentage of total time, for example, 60%–90%. The reverse metric can also be used, for example, with time that the glucose readings are above or below the range being kept below 20% or less, or 10% or less as a goal. For example, for a patient currently on basal-only therapy, the basal dose may be optimized to control the overnight glucose levels, but the daytime glucose levels may still be high. Increasing the basal dose to bring down the daytime levels would cause the overnight levels to fall below the target range, so meal-time insulin could be added.
[0073] At a high level, the methods below all follow these steps. First, determine glucose patterns for each key time-of-day period (for example overnight, post-breakfast, post- lunch, post-dinner), which is accomplished by data processing algorithm 210 as discussed above. Second, optimize current therapy. Third, detect when optimization is substantially achieved. Fourth, recommend an additional dose or additional or alternative therapies. [0074] For step three, periodically over time, or concurrent with each titration step, an assessment is done to determine if the patient’s current therapy is substantially optimized. Substantial optimization is needed because even if the patient’s therapy is sufficiently personalized, glucose patterns may still change from time to time due to change in patient life-style, eating habits, physical condition, etc. Various means may be used to determine if the patient’s therapy is substantially optimized. In some embodiments, the conditions for optimization basal and prandial doses are the following: [0075] Basal dose is substantially optimized when overnight glucose pattern is High/Low, Moderate/Low or in target. [0076] Meal dose is optimized when associated post-meal glucose pattern is High/Low, Moderate/ Low, or in target. [0077] When these conditions are met for the basal dose and each meal dose currently part of the regimen, then the MDI regimen can be considered substantially optimal. [0078] For the fourth step, glucose patterns can be determined for the periods not associated with a meal dose in the current regimen. For the some embodiments, the recommendation is made as part of report to a health care professional. The report may indicate all of the non-dose post-meal periods with a High Pattern, with a recommendation to consider one or all of these for starting an associated meal dose. In addition, the report may indicate the mean or median glucose for each of the non-dose periods with a High Pattern, as a way to further distinguish the health care professional’s and patient’s choice for where to initiate the dose; alternatively, the report may just indicate the non-dose period with the High Pattern and the highest mean or median glucose. The report may also only indicate the non-dose period with the High Pattern that occurs first in the day. Identifying this period can help a health care professional mitigate the High Pattern earlier in the day, which can in turn address a High pattern later in the day as a follow-on effect. [0079] If the pattern for the non-dose period is High/Low then the report may indicate that initiating a meal dose should be accompanied by a reduction in a basal insulin dose
(e.g., the basal dose prior to that High/Low period). Otherwise the High/Low pattern periods may be treated the same as the High pattern periods described above. [0080] The report may also provide guidance regarding the amount of the initiated insulin dose. The additional dose may be calculated as a percentage of the basal dose or a percentage of the total daily dose, or a factor depending on the body weight of the patient (e.g., the number of units/kg). [0081] FIG.6 is a process flow diagram of an embodiment of therapy algorithm 220. As seen at the top of the flow diagram, there are two inputs into therapy algorithm 220: the output 221 (the glycemic risks) of data processing algorithm 210, and a copy of the patient’s existing therapy 222 (which can be sourced, for example, from the patient’s medical record, entry by the HCP, and/or entry by the patient). Embodiments of therapy algorithm 220 discussed now will be based on the identification and initiation of prandial insulin for patients currently using basal insulin. However, as discussed below, therapy algorithm 220 can be adapted for patients with different therapies. [0082] In a first step 223, the algorithm determines if the basal insulin dose is substantially optimized. Basal insulin is considered to be substantially optimized when (1) the basal dose is set at the maximum dosing value that does not trigger LLG 217 beyond a given threshold (e.g., the “High” threshold discussed above) for any time of day period, and (2) the basal dose does not result in median glucose 215 being rated either Medium or High with LLG 217 being rated a Medium risk. If either of these conditions are not met, then therapy algorithm 220 follows an output 223a to produce instructions 230 to optimize the basal insulin dose until these conditions are met. This optimization can be accomplished by recommending gradual increases (e.g., 0.5U, 10% increases) to the existing basal insulin dose until either of the conditions above are met, or the maximum dosage limit is met. In some embodiments, therapy algorithm 220 may include instructions to titrate the basal insulin dose in the same manner until LLG 217 is no longer a risk by updating instructions 230 to suggest a lower basal dose. If the basal insulin dose is already at a maximum allowable dosage, then therapy algorithm 220 does not suggest any further increases of the basal insulin dose and proceeds as if the basal dose is optimized. [0083] If the basal dose is optimized in step 223, then in a step 224 therapy algorithm 220 determines if any time of day periods 212 have a median glucose that is rated Medium or High. If this is the case, then therapy algorithm 220 can calculate an update 225 in one of
two ways. In one embodiment, therapy algorithm 220 may output instructions 230 to add a prandial insulin dose to the meal associated with the time of day period 212 in which glucose median is rated as Medium or High, or the meal that occurs immediately before the relevant time of day period 212. In a second embodiment, if multiple time periods have a glucose median rated as Medium or High, therapy algorithm 220 may output instructions 230 to add a prandial dose to the meal associated with only the earliest of the multiple time periods 212 that was selected per the analysis above. This can be helpful because administering prandial insulin at the earliest applicable instance can improve glucose levels throughout the remainder of the day. This potentially can allow a patient to reduce the need to administer additional doses of prandial insulin. In another embodiment, when multiple time of day periods 212 have median rated as Medium or High prandial insulin can be recommended for the time of day period 212 with the highest median glucose level. Otherwise, if glucose median is in target and not Medium or High for any period then algorithm 220 determines prandial medication is not needed. [0084] Therapy algorithm 220 may also include instructions to consider reducing any existing prandial doses to avoid hypoglycemia. For example, if a high LLG 217 risk is detected in a time of day period 212 corresponding to a prandial insulin dose, the prandial insulin dose may be lowered (e.g., by at least a predetermined minimum dose). If time of day period 212 is overnight then if there is a prandial insulin dose corresponding to the last meal of the day, that dose may be lowered. If there is no prandial insulin dose corresponding to the last meal, therapy algorithm 220 can suggest a reduction in basal insulin instead of an adjustment to prandial insulin doses earlier in the day. [0085] These features of therapy algorithm 220 can also be used to address overbasalization. For example, if LLG 217 risk is High, but glucose median is OK, therapy algorithm 220 may check if the patient is currently on basal insulin. If that is the case, then therapy algorithm 220 can produce a recommendation to reduce the basal insulin dose to address the overbasalization. This recommendation could be extended to other slow-acting treatments, such as GLP-1RA dosing, among others. Thus, therapy algorithm 220 may recommend a reduction in both basal insulin and other treatments to address the high LLG 217 risk. Additionally, therapy algorithm 220 may also be programmed to suggest alternative or additional therapy options. For example, therapy algorithm 220 may suggest a reduction in basal insulin, but the addition of another
treatment, such as a prandial insulin dose, to address the expected rise in median glucose levels during the day. [0086] The embodiments of therapy algorithm 220 discussed above can also function if a prandial dose is already being administered by the patient. In that case, therapy algorithm 220 will check if any of the time periods 212 detected in step 224 already have a prandial dose associated with the corresponding meal. If this is the case, then instructions 230 are modified to increase the relevant prandial dose from its current dose to the next suitable greater dosing. In some embodiments, the prandial doses may be determined as a percentage of a total daily basal dose (i.e., the total of all basal doses administered during a day). For example, the added prandial dose may begin at 10% of the total basal dose. When titrating the dose can be increased by increments of 2%, 5%, 10%, 15%, or 20% of the total basal dose, among others each iteration. [0087] If the prandial dose is already at a maximum, therapy algorithm 220 can be programmed to suggest adding a second prandial dose to the next earliest time period 212. As discussed above, therapy algorithm 220 will also suggest adding additional prandial doses to address any other time of day period 212 that have output data 221 that indicate a need to manage an excessively high glucose level. [0088] Therapy algorithm 220 is designed to be run iteratively or repeatedly each day (or any predetermined number of days), and thus to titrate the existing prandial dosing. These titrations can occur based on data that is averaged across a desired time period. For example, that relevant data may be taken as the average of the most recent ten day period. The prandial dosing can be increased to a predetermined maximum dose. The frequency of iteration of therapy algorithm 220 can also be reduced as desired, which can reduce battery usage if therapy algorithm 220 is being operated on a battery-powered device. For example, therapy algorithm 220 can be set to operate once every predetermined period (e.g., daily, weekly, every two weeks, monthly) or after a predetermined event (e.g., a new glucose sensor is activated or a change to a setting is received from a user). In some embodiments, the frequency of iteration may change dynamically. For example, after running daily for a set period, therapy algorithm 220 may reduce iteration frequency, such as to weekly. This can have the benefit of establishing an initial dosage regimen and then maintaining that regimen in a less resource-intensive manner. [0089] The embodiments described herein provide several benefits over existing treatment strategies. First, data processing algorithm 210 leverages the continuous data of
CGM 100 to provide a complete picture of a patient’s glucose levels throughout the day. This enables both more accurate and earlier detection of increased glucose levels that are not controlled by basal insulin therapy, which improves patient outcomes. Further, because therapy algorithm 220 considers optimizing the basal insulin dosing before recommending the addition of prandial insulin, the risk of unnecessary prandial insulin therapy is minimized. Additionally, the granularity of the output of data processing algorithm 210 allows for therapy algorithm 220 to target the prandial insulin where it is most needed, which also minimizes the burden on the patient regarding adding an additional medical therapy. [0090] Therapy algorithm 220 can also implement treatments with different medications, such as a frontline, non-insulin medication, basal insulin, or pre-mixed insulin. Data processing algorithm 210 and its output remains unchanged in such embodiments. The overall process that therapy algorithm 220 follows is largely unchanged, but certain analysis steps may be weighed differently, or omitted altogether, depending on which medication is being considered for initiation or updating. For example, for a patient that is currently on no relevant therapies, algorithm 220 may recommend a non-insulin based medicine(e.g., GLP-1, SGLT2 inhibitors, metformin, or other oral medication etc.) for initiation. FIG.7 shows this variant of algorithm 220. Steps 223 and 224 (the steps above related to analysis of glucose median 215, LLG 217, and glucose variability 218) remain the same here. Step 225 is modified to accommodate the details regarding the specific timing of any increases in glucose levels, and corresponding recommendations to initiate treatment at the relevant meal, because a typical non-insulin treatment is a slow-acting, single daily dose medication. Thus, there is no need to determine a specific time period 212 to recommend adding the dose to based on the existing therapy 222 and data output 221. There may be recommendations based on the type of medication being initiated that are incorporated into instructions 230. This could include, for example, timing of medication administration unrelated to the analyzed data 221. In these embodiments, comparisons like glucose median 215 may be weighed more heavily because there is less concern about any existing medication resulting in an excessively low glucose readings. For example, the result of the analysis of glucose median 215 may be weighed to override all but the highest risk of low glucose. [0091] FIG.8 shows an embodiment of therapy algorithm 220 adapted for recommending the implementation of a basal insulin therapy. This algorithm is generally similar to the
embodiments of FIG.7 in that the specific timing of application of the basal insulin dose is not considered when analyzing the data. However, unlike the embodiment of FIG.7, analysis variables like LLG 217 are given substantial weight because the existence of other therapies does result in a concern for low glucose. For example, LLG 217 may not be considered in the embodiment of FIG.7, but may be given the same analytical weight as discussed with respect to the embodiment of FIG.6 in this embodiment. The remaining elements of therapy algorithm 220 otherwise function as discussed above. [0092] It should be understood that any relevant medication can be applied to therapy algorithm 220 with suitable modifications as discussed above based on the specifications of the medicine. In some embodiments, therapy algorithm 220 has the ability to determine a recommended therapy for suitable medications, and is able to select the medicine to be applied to a patient based on the patient’s medical history. For example, if the patient is not on any relevant medications, therapy algorithm 220 will default to determining whether to recommend initiation of a frontline therapy. This technique can also apply to the addition of other therapies. For example, in the embodiment of FIG.6 discussed above, therapy algorithm 220 can perform a check to determine if there are any other applicable therapies that may be added in conjunction with, or in place of, recommending a prandial insulin dose. An example of such a therapy may be a glucagon-like peptide‑1 receptor agonist (GLP-1 RA). Therapy algorithm 220 can be programmed to maximize the dose of these other therapies in a step-wise fashion before or along with recommending another medication (such as prandial insulin in the embodiment of FIG. 7). [0093] Output instructions 230 can be displayed and used in several different ways. Reader device 120 can receive output instructions 230. In some embodiments, reader device 120 is a computing device or mobile device of the patient, and the patient can review the output instructions 230. In some embodiments, the health care professional has a reader device 120 in the form of a computing device associated with the health care professional, who can review output instructions 230 and determine whether to initiate treatment. In these embodiments, the patient may receive a copy of output instructions 230, or may only receive a notification that output instructions 230 are available. Other data may also be included with output instructions 230, for example, including the data received from continuous glucose monitor 100.
[0094] As discussed above, therapy algorithm 220 can include optimization of multiple medications. In these situations, a health care professional must track the patient’s medications and prescriptions and update them as needed to ensure the patient has access to sufficient medication for the therapies recommended. This can be time consuming and result in errors that affect the patient’s therapies. Thus, there exists a need for improved tracking of medications. [0095] In some embodiments, therapy algorithm 220 can also be programmed to track any or all the medication(s) needed for a patient’s therapy to facilitate prescription fulfilment. For example, basal insulin, prandial insulin, pre-mixed insulin, GLP-1 RA, and other medications may be included in these embodiments. Therapy algorithm 220 can calculate the total existing dosage of medication(s) per the patient’s history by retrieving the existing dosage from the patient’s medical record, or by having the patient or health care professional update the algorithm with the existing dosage manually. That total existing dosage can be added to output instructions 230. In this way, the patient, health care professional, or others with access to output instructions 230 can see the medication(s) needed to meet the current, existing therapy. [0096] In some embodiments, if output instructions 230 include a suggested therapy change, therapy algorithm 220 can also update output instructions 230 (e.g., recommended prescription update/change) to provide the medication(s) needed for the updated therapy. In some embodiments, the updated medication(s) can be provided in a format that can be used by the health care professional to order new or revised prescriptions for the patient. This helps ensure the patient is able to continue their therapy without running out of medication. Therapy algorithm 220 can process the daily medication(s) needed from the total medication required into values suitable for order as a prescription for a set time period. For example, if a total amount of insulin is approximately 100 U/mL per day, therapy algorithm 220 can propose ordering or prescribing 10100U/mL 3mL insulin vials, which would amount to a 100 U/mL daily supply for thirty days. This determination of required medication can also be based on an average of recommended doses over a predetermined time period (e.g., the past week or month). Any suitable time period or medication packaging size can be accounted for by therapy algorithm 220, and this data can be updated to account for changes in available medication. Therapy algorithm 220 may pause recommending increasing medication or initiating new therapy and may alert the health care professional via output instructions
230 if the existing prescribed medication does not include the increased dosage or new therapy. [0097] In some embodiments, output instructions 230 may be received by a medication delivery device 108 as shown in FIG.9. Medication delivery device 108 may be any suitable system that is able to deliver or administer medication to the patient. As seen in FIG.9, medication delivery device 108 is operably connected to sensor control device 102, reader device 120, and local computing system 170. For example, medication delivery device 108 may be a system that is attached to or implanted in the patient and is able to dispense medications, such as basal insulin, pre-mixed insulin, or prandial insulin. Other examples of medication delivery device 108 can include an infusion pump, a patch pump, or an injection device such as an injection pen. In some examples, medication device 108 may be a smart insulin pen cap that is associated with an insulin pen. Output instructions 230 can be received by medication delivery device 108 and used to implement a treatment plan as recommend by output instructions 230. In another example, output instructions 230 can be received by local computer system 170 and/or reader device 120, and used to implement a treatment plan as recommended by output instructions 230. These actions may require approval of one or both of the patient (e.g., at medication delivery device 108, local computer system 170, or reader device 120) and/or the patient’s health care professional. In some examples, these output instructions 120 may require approval by the patient’s health care professional before they are transmitted to the medication delivery device 108, local computer system 170, or reader device 120. [0098] An example of a method of operation incorporating medication delivery device 108 includes the operation of therapy algorithm 220 as discussed above. In this method, output instructions 230 are sent directly to medication delivery device 108, which is configured to deliver medication to the user (automatically or with further input by the user). Medication delivery device 108 may include a controller programmed to determine if the desired therapy is available for delivery. If the desired therapy is not available, medication delivery device 108 may be programmed to deliver an alert to the user or to a medical professional, or both, through any suitable communication method. In one embodiment, medication delivery device 108 may prompt the user or a health care professional for confirmation before implementing the therapy recommended in output instructions 230. In a second embodiment, medication delivery device 108 may proceed with implementing the therapy without any prior authorization. This process may be
repeated for every updated output instruction 230 that is received by medication delivery device 108. [0099] In some embodiments, therapy algorithm 220 can receive data from medication delivery device 108 regarding the actual medication administered to the patient. It should be understood that medication delivery device 108 here can include any and all medication delivery devices, such as medication information from an infusion pump, a smart injection pen, a dose monitoring add-on of an injection pen (e.g., a dose monitoring cap), among others, and could also include manual entry for non-connected devices. In this way, therapy algorithm 220 tracks the actual medication administered, which can improve medication tracking as discussed above. [0100] It is to be appreciated that the Detailed Description section, and not the Summary and Abstract sections, is intended to be used to interpret the claims. The Summary and Abstract sections may set forth one or more but not all exemplary embodiments of the present invention as contemplated by the inventor(s), and thus, are not intended to limit the present invention and the appended claims in any way. [0101] The foregoing description of the specific embodiments will so fully reveal the general nature of the invention that others can, by applying knowledge within the skill of the art, readily modify and/or adapt for various applications such specific embodiments, without undue experimentation, without departing from the general concept of the present invention. Therefore, such adaptations and modifications are intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. It is to be understood that the phraseology or terminology herein is for the purpose of description and not of limitation, such that the terminology or phraseology of the present specification is to be interpreted by the skilled artisan in light of the teachings and guidance. [0102] The breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents. [0103] Exemplary embodiments are set forth in the following numbered clauses: 1. A method of determining an optimized dosage regimen for a glucose disorder, the method comprising: receiving existing insulin dosage information for a patient at a computing device;
receiving, by the computing device, glucose data from a patient using a continuous glucose monitor comprising a first portion arranged above a skin surface of the patient and a second portion arranged below the skin surface and in contact with interstitial fluid of the patient; determining glycemic risks for a time period based on the received glucose data; determining that the existing insulin dosage is optimized based on the glycemic risks; determining the need for a prandial insulin dose if the existing dosage is optimized and if the glycemic risks indicate an elevated risk of a high glucose level in the time period; and outputting instructions comprising a recommendation of the prandial insulin dose to the computing device using a communication system. 2. The method of clause 1, wherein the glycemic risks include a likelihood of low glucose, preferably determined by taking a sum of the differences between a plurality of glucose measurements and a predetermined glucose value and dividing the sum by a total number of the plurality of glucose measurements. 3. The method of clause 1 or 2, wherein the elevated risk of a high glucose level is determined by calculating a median glucose for the time period and comparing the median glucose to at least one predetermined glucose value. 4. The method of clause 1 or 2, wherein the elevated risk of a high glucose level is determined by calculating a median glucose for the time period. 5. The method of any of the preceding clauses, wherein the time period is one of a plurality of time periods, the method further comprising separating the glucose data into data sets based on the plurality of time periods, respectively, wherein the processing, comparing, determining and outputting steps are performed on each of at least two data sets separately. 6. The method of any one of the preceding clauses, where the time period is one of a plurality of time periods, the method further comprising separating the glucose data into data sets based on the plurality of time period. 7. The method of clause 5 or clause 6, wherein each time period is associated with an event that occurs during the time period.
8. The method of clause 5 or clause 6, wherein at least one time period of the plurality of time periods is associated with an event that occurs during the at least one time period. 9. The method of clause 7 or clause 8, wherein the event is a meal. 10. The method of any preceding clause, further comprising: determining if the existing dosage information includes a prandial insulin dosage; and adjusting the existing prandial insulin dosage by increasing the prandial insulin dosage when the glycemic risks indicate an elevated risk of a high glucose level and when the existing insulin dosage is at a predetermined maximum. 11. The method of any of clauses 5-9 further comprising: determining if the existing insulin dosage is optimized and if the glycemic risks indicate an elevated risk of a high glucose level in more than one time period; and recommending a prandial insulin dose for the first of the one or more time periods when the glycemic risks indicate an elevated risk of a high glucose level and when the existing insulin dosage is at a predetermined maximum in more than one time period. 12. The method of any of clauses 4 to 11, further comprising: determining if the existing insulin dosage is optimized and if the glycemic risks indicate an elevated risk of a high glucose level and when the existing basal insulin dosage is at a predetermined maximum in more than one time period; and recommending a prandial insulin dose for the time period with the highest glucose median. 13. The method of any preceding clause, further comprising modifying an existing basal insulin dosage to optimize the existing basal insulin dosage if the existing basal insulin dosage is below a predetermined maximum amount, and if the glycemic risks indicate an elevated risk of a high glucose level in the time period and the glycemic risks do not indicate an elevated risk of low glucose in the time period. 14. The method of any one of clauses 1–13, further comprising: determining if the glucose data indicates an overbasalization; and updating the output instructions to recommend a reduction in basal insulin. 15. The method of clause 14, wherein determining if the glucose data indicates an overbasalization comprises comparing the difference between a maximum and a minimum glucose in an overnight time period to a predetermined difference amount, and
determining that overbasalization is occurring if the difference exceeds the predetermined difference level. 16. The method of clause 14 or 15, wherein determining if the glucose data indicates an overbasalization comprises calculating the slope of a line fit to the decrease in glucose in an overnight time period, and comparing the slope to a predetermined slope, wherein determining that overbasalization is occurring if the slope exceeds the predetermined slope. 17. The method of any one of clauses 14-16, wherein determining if the glucose data indicates overbasalization includes using one of a running average or weighted average of glucose data taken from multiple days. 18. The method of any one of clauses 1-17, further comprising: receiving the patient’s existing medications; adding the existing medications to the output instructions; and updating the existing medications in the output instructions to accommodate a modification to an existing dosage 19. A system for determining a dosage regimen for a glucose disorder, the system comprising: a continuous glucose monitor, comprising: a glucose sensor comprising a first portion configured to be arranged above the skin and a second portion configured to be arranged below the skin and in contact with interstitial fluid to sense glucose within the interstitial fluid; and sensor electronics coupled to the glucose sensor and comprising a memory, communication circuitry, and processor coupled to the memory and communication circuitry; a computing device including a processor and memory, the computing device operably linked to the continuous glucose monitor, wherein the memory stores instructions that when executed by the processor cause the processor to: receive the glucose data from the continuous glucose monitor; retrieve existing insulin dosage information from the memory; determine glycemic risks for a plurality of time periods based on the received glucose data;
determine that the existing insulin dosage is optimized based on the glycemic risks; determine the need for a prandial insulin dose if the existing dosage is optimized and if the glycemic risks indicate an elevated risk of a high glucose level in the time period; and output instructions comprising a recommendation of the prandial insulin dose to the computing device. 20. The system of clause 19, wherein the memory further comprises instructions that instruct the processor to calculate risk of low glucose by taking a sum of the differences between a plurality of glucose measurements and a predetermined glucose value and dividing the sum by a total number of the plurality of glucose measurements. 21. The system of clause 19 or 20, wherein the memory further comprises instructions that instruct the processor to determine the elevated risk of high glucose by calculating the median glucose and comparing the median glucose to a predetermined value to determine a risk. 22. The system of an one of clauses 19-21, wherein the elevated risk of a high glucose level is determined by calculating a median glucose for the time period. 23. The system of any one of clauses 19-22, wherein the time period is one of a plurality of time periods, and wherein the instructions further cause the processor to separate the glucose data into data sets based on the plurality of time periods, respectively, wherein the processing, comparing, determining and outputting steps are performed on each of the two data sets separately. 24. The system of any one of clauses 19 to 23, where the time period is one of a plurality of time periods, the method further comprising separating the glucose data into data sets based on the plurality of time period. 25. The system of clause 24, wherein each time period is associated with an event that occurs during the time period. 26 The system of clauses 23 or 24, wherein at least one time period of the plurality of time periods is associated with an event that occurs during the at least one time period. 27. The system of any one of clauses 19-26, wherein the instructions further cause the processor to:
determine if the existing insulin dosage is optimized and if the glycemic risks indicate an elevated risk of a high glucose level in more than one time period; and recommend a prandial insulin dose for the first of the one or more time periods when the glycemic risks indicate an elevated risk of a high glucose level and when the existing insulin dosage is at a predetermined maximum in more than one time period. 28. The system of any one of clauses 19-27, wherein the instructions further cause the processor to: determine if the existing dosage information includes a prandial insulin dosetreatment; and adjust the existing prandial insulin dose by increasing the prandial insulin dosetreatment when the glycemic risks indicate an elevated risk of a high glucose level and if the existing insulin dosage is optimized. 29. The system of any of clauses 26 to 27, wherein the computing device comprises a display that is configured to display the dosage instructions. 30. The system of any of clauses 26 to 29, wherein the instructions further cause the processor to modify an existing basal insulin dosage to optimize the existing basal insulin dosage if the existing basal insulin dosage is below a predetermined maximum amount, and if the glycemic risks indicate an elevated risk of a high glucose level in the time period and the glycemic risks do not indicate an elevated risk of low glucose in the time period. 31. The system of any one of clauses 19-30, wherein the instructions further cause the processor to determine if the glucose data indicates an overbasalization; and update the output instructions to recommend a reduction in basal insulin. 32. The system of clause 31, wherein determining if the glucose data indicates an overbasalization comprises comparing the difference between a maximum and a minimum glucose in an overnight time period to a predetermined difference amount, and determining that overbasalization is occurring if the difference exceeds the predetermined difference level. 33. The system of clause 31 or 32, wherein determining if the glucose data indicates an overbasalization comprises calculating the slope of a line fit to the decrease in glucose in an overnight time period, and comparing the slope to a predetermined slope, wherein determining that overbasalization is occurring if the slope exceeds the predetermined slope.
34. The system of any one of clauses 31 to 33, wherein determining if the glucose data indicates overbasalization includes using one of a running average or weighted average of glucose data taken from multiple days. 35. The system of any one of clauses 19-34, wherein the instructions further cause the processor to: receive the patient’s existing medications; add the existing medications to the output instructions; and update the existing medications in the output instructions to accommodate a modification to an existing dosage.