Trend-based insulin dose adjustment

Abstract

Techniques related to trend-based adjustment of insulin dose are disclosed herein. In some embodiments, these techniques may involve obtaining glucose values and rates of glucose change (ROC) based on glucose measurements performed on a person. An initial amount of insulin delivered to the person is calculated based on the glucose value. However, an adjustment value may be obtained in response to determining that the glucose value exceeds the glucose threshold and the glucose ROC exceeds the ROC threshold. The adjustment value represents an adjustment to be applied to the initial amount of insulin when both the glucose threshold and the ROC threshold are exceeded. The adjustment value may be predetermined based on an earlier glucose measurement performed on the person. For example, the adjustment value and the threshold may be collectively determined by a computer simulation using a human-specific physiological model.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63 / 583,095, filed on September 15, 2023, entitled “TREND BASED ADJUSTMENTS TO INSULIN DOSAGE,” which is incorporated herein by reference in its entirety. Technical Field

[0002] The present disclosure relates generally to medical devices, and more particularly to therapy delivery devices and systems including therapy delivery devices. Certain aspects of the present disclosure relate to techniques for adjusting insulin dosage in conjunction with glucose monitoring. Background Art

[0003] Some medical conditions, such as diabetes, require monitoring of the patient's corresponding physiological condition (e.g., glucose level) in order to determine when to deliver a dose of therapeutic agent. For example, delivery can be automated using a continuous glucose monitoring (CGM) device that controls an insulin infusion pump based on glucose measurements obtained by the CGM device. Therapeutic agents can also be delivered manually, such as by injecting a pre-measured dose of insulin using a syringe. Glucose monitoring is traditionally performed on a blood sample, such as using a finger stick measurement. Blood glucose measurement is becoming less and less common compared to alternative sensing methods such as interstitial glucose sensors, because these sensing methods allow glucose to be measured in a more convenient manner and with less discomfort.

[0004] The amount of a dose is sometimes determined based on the patient's physiological condition at the time the dose is delivered or before and after delivery. For example, the number of units of insulin to be delivered can be determined based on the patient's glucose level measured by a blood glucose meter, interstitial glucose sensor, or other glucose sensing device. When the dose is determined based on sensor measurements, the accuracy of the measurement may significantly affect the patient's health outcomes. In the case of insulin delivery, too much insulin may cause hypoglycemia, while too little insulin may cause hyperglycemia. In addition, rapid changes in physiological conditions may invalidate the measurement, even if the dose is delivered shortly after the measurement (e.g., within a few minutes). Therefore, determining an appropriate dose can be challenging in the case of changing physiological conditions. Summary of the invention

[0005] Disclosed herein are examples of medical devices and related systems and methods for adjusting the dosage of a drug or therapeutic agent based on detecting trends in the physiological condition of a person receiving the dosage. In certain aspects, the present disclosure relates to adjusting the insulin dosage when both the glucose level and the glucose rate of a person exceed corresponding thresholds. Various aspects of the present disclosure also relate to determining a glucose level threshold and a glucose rate of change threshold, which, if exceeded, indicates that hypoglycemia or hyperglycemia excursion may occur without insulin dosage adjustment. In some embodiments, the threshold and the corresponding adjustment factor are determined by running a computer simulation on a physiological model customized for a person receiving the dosage. These techniques can be practiced in a variety of ways, such as using: a processor-implemented method; a system comprising one or more processors and one or more processor-readable media; and / or one or more (non-transient) processor-readable media.

[0006] In some embodiments, these techniques may involve obtaining glucose values ​​and glucose rate of change (ROC) values ​​based on glucose measurements performed on a person. An initial amount of insulin delivered to the person is calculated based on the glucose values. These techniques may also involve determining that a glucose value exceeds a glucose threshold and that a glucose ROC exceeds a ROC threshold. An adjustment value may be obtained in response to the determination, the adjustment value representing an adjustment to be applied to the initial amount of insulin when the glucose threshold and the ROC threshold are exceeded. The initial amount of insulin is then adjusted according to the adjustment value. The adjustment value may be determined based on an earlier glucose measurement performed on the person. For example, an earlier glucose measurement may have been used to create a human-specific physiological model for the simulation discussed above.

[0007] This summary is neither intended to identify the main or essential features of the claimed subject matter nor is it intended to be used alone to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of the disclosure, any or all of the drawings, and each claim. The foregoing and other features and examples will be described in more detail in the following description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The above and other aspects and features of the present disclosure will become more apparent in view of the following detailed description when considered in conjunction with the accompanying drawings, wherein like reference numerals identify like elements.

[0009] Figure 1 Exemplary therapy systems according to aspects of the present disclosure are shown.

[0010] Figure 2 An example of a glucose model that may be used to perform simulations in accordance with aspects of the present disclosure is shown.

[0011] Figure 3An example of a memory subsystem storing adjustment-related information according to aspects of the present disclosure is shown.

[0012] Figure 4 Examples of adjustment parameters according to aspects of the present disclosure are shown.

[0013] Figure 5A and Figure 5B An example of glucose drift rate as a function of glucose change rate is shown.

[0014] Figure 6 Shown is the relationship between time in range and time below range in a sample population when the same set of tuning parameters is used for the entire population.

[0015] Figure 7 Comparisons of time in range with and without dose adjustments were included.

[0016] Figure 8 Exemplary tuning parameter values ​​for determining tuning parameter sets according to aspects of the present disclosure are shown.

[0017] Fig.9A and Fig. 9B An example of the results of insulin adjustments is shown.

[0018] Fig. 10A and Fig. 10B Another example of the results of insulin adjustments is shown.

[0019] Fig.11 An exemplary process for adjusting dosage according to aspects of the present disclosure is shown.

[0020] Fig.12 An exemplary process for determining adjustment parameters according to aspects of the present disclosure is shown.

[0021] Fig.13 An exemplary process for coverage adjustment according to aspects of the present disclosure is shown.

[0022] Fig.14 Exemplary therapy delivery systems according to aspects of the present disclosure are shown.

[0023] Fig.15 An exemplary insulin delivery device that can be used to implement one or more embodiments described herein is shown. DETAILED DESCRIPTION

[0024] Disclosed herein are examples of medical devices and related systems and methods for adjusting insulin dosage based on detecting trends in the physiological condition of people receiving the dosage. Various aspects of the disclosure relate to providing a method specific to a person for adjusting insulin dosage when glucose levels change rapidly. Various aspects of the disclosure also relate to determining adjustment parameters and applying these adjustment parameters in a therapy environment. In some instances, the adjustment parameters may be ignored for the purpose of setting the amount of the dosage.

[0025] Although the present disclosure is mainly described about insulin delivery systems, the scope of the present disclosure is not limited to insulin delivery systems. On the contrary, the present disclosure is equally applicable to other therapy systems and can be implemented with other therapy systems. For example, in some embodiments, the technology of the present disclosure can be practiced in relation to pain therapy.

[0026] Discussions utilizing terms such as, for example, "process," "compute," "determine," "establish," "analyze," "examine," etc. may refer to operations and / or processes performed by a computer, computing platform, computing system, or other electronic device equipped with one or more hardware-implemented processing units (e.g., microprocessors).

[0027] Unless explicitly stated, the methods described herein are not limited to a particular order or sequence. In addition, some of the methods or elements of the methods or elements may occur or be performed simultaneously or in parallel. In some instances, the methods described involve operations and / or processes performed by different electronic devices that are communicatively coupled to each other to form a therapy delivery system. Unless otherwise specified or prohibited by the context, an operation or process performed by one device (e.g., a server computer) may also be performed in whole or in part by another device in the therapy delivery system (e.g., a computing device communicating with a server).

[0028] Figure 1 An example of a therapy delivery system 100 is shown, which includes a monitoring device 110, a delivery device 120, and a computing system 130 communicatively coupled via a set of communication links 102. The communication links 102 may include wired and / or wireless connections. In some examples, the communication links 102 may form one or more communication networks. For example, the communication links 102 may be part of the Internet, a cellular network, a Wi-Fi network, a local area network, etc. Figure 1 1 to illustrate one manner in which functionality related to dose determination may be distributed across different devices in a therapy delivery system. Fig.14 (described below) shows another example of a therapy delivery system having similar components, but showing in greater detail how the various components may be communicatively coupled.

[0029] Monitoring device 110 is configured to obtain measurements from sensor 112 and determine a dose of therapeutic agent (e.g., insulin) to be delivered by delivery device 120 based on the measurements. Sensor 112 is configured to measure a physiological condition of person 105. Figure 1 As shown in , sensor 112 can be part of monitoring device 110. For example, monitoring device 110 can be a CGM device, and sensor 112 can be an interstitial glucose sensor integrated into the CGM device. Thus, monitoring device 110 and sensor 112 can be a single unit worn on or attached to the body of person 105. However, in some instances, sensor 112 can be remote from monitoring device 110, such as when sensor 112 is implanted within the body of a person.

[0030] The monitoring device 110 is equipped with one or more processing units 114 that process the measurements from the sensors 112 to determine the timing of the dose and the amount of the dose. The sensors 112 may send the measurements to the monitoring device 110 as a numerical value (e.g., a digitally encoded glucose value) that characterizes the physiological condition. Alternatively, the sensors 112 may output the measurements in the form of one or more electrical signals generated under the influence of the physiological condition. Thus, the processing unit 114 may be configured to convert the value of the one or more electrical signals (voltage, current, impedance, etc.) into a corresponding measurement value.

[0031] Monitoring device 110 can track sensor measurements to detect when the triggering condition for drug administration is met, thereby determining the timing of the dosage. In diabetes management, insulin dosage is usually divided into a basal dose or a bolus dose. The delivery frequency of the basal dose is low (e.g., once or twice a day), and is designed to keep blood sugar levels stable throughout the day, usually using slow-acting insulin. The amount of the basal dose may vary from patient to patient, but the dose each time is usually the same. In contrast, the bolus dose is designed to offset the effects of carbohydrates consumed during meals. The bolus dose is usually carried out before meals, but can also be delivered after meals, usually using rapid-acting insulin. Aspects and embodiments of the present disclosure can be practiced with one or more types of insulin (e.g., rapid-acting insulin, intermediate-acting insulin and / or slow-acting insulin). For example, delivery device 120 can be configured to use rapid-acting insulin for basal doses and bolus doses.

[0032] Based on the above discussion, it will be understood that the triggering conditions for the basal dose may be different from the triggering conditions for the bolus dose. For example, the monitoring device 110 may be programmed or otherwise configured to initiate the basal dose at a certain time of the day and initiate the bolus dose in response to a user input indicating that a meal is about to be eaten. Therefore, the monitoring device 110 may include one or more input / output (I / O) devices 116 to interact with a user (e.g., person 105). Examples of I / O devices 116 include display monitors, audio speakers, touch screens, tactile feedback devices (e.g., for giving vibration alerts), keyboards or keypads, mice or tracking pads, etc. The I / O device 116 can be used to notify the user of their glucose level and the amount of insulin delivered. The I / O device 116 can also be used to provide information about the user to the monitoring device 110. For example, the user input of the I / O device 116 may include age, height, weight, insulin sensitivity factor (ISF), insulin / carbohydrate ratio (ICR) and / or other information about the person 105 that the processing unit 114 can use to calculate the amount of the dose.

[0033] The monitoring device 110 also includes a memory subsystem 118. The memory subsystem 118 may be formed using volatile and / or non-volatile memory devices, such as random access memory, read-only memory, flash memory, solid-state drive, hard disk drive, etc. In some implementations, the memory subsystem 118 stores instructions executable by the processing unit 114 to cause the monitoring device 110 to calculate a dose amount and transmit the calculated dose to the delivery device 120. For example, the memory subsystem 118 may store program code that implements a bolus calculation algorithm according to the following formula: (1) in B is the bolus volume to be delivered, CARB is the amount of carbohydrates expected to be consumed (or consumed in the near future), ICR is a person's insulin / carbohydrate ratio, ISF It is the insulin sensitivity factor of humans. Target is the target glucose level (e.g., a value between 70 and 180 milligrams per deciliter (mg / dL)), and BG It is a person's blood sugar level. ICR Indicate the amount of carbohydrate (e.g., in grams) that one unit of insulin is expected to cover. ISF Express the amount by which one unit of insulin is expected to lower blood sugar (e.g., in mg / dL). IOB Stands for insulin in body and indicates the amount of remaining insulin from an earlier dose remaining in a person's body.

[0034] As described above, sensor 112 may be an interstitial glucose sensor. Interstitial glucose tends to lag behind blood glucose, for example, by several minutes or longer. This is because glucose takes time to diffuse from the bloodstream into the interstitial fluid between the skin and the blood vessels. Monitoring device 110 may be configured to estimate BG from sensor glucose (SG) taking into account this lag when measuring from the interstitial glucose sensor. Alternatively, in order to reduce the computational burden of processing unit 114, monitoring device 110 may replace BG with SG when calculating the bolus dose according to equation 1. When SG is used instead of BG, when delivering a dose under conditions where the lag is expected to have a disproportionately negative impact on the efficacy of the calculated amount, monitoring device 110 may compensate for sensor lag by adjusting the dose amount. Such adverse conditions correspond to times when the sensor glucose value changes rapidly (e.g., increases or decreases with a rate of change (ROC) exceeding a certain magnitude in either direction).

[0035] As described in detail below, adjustments can be applied using person-specific adjustment parameters, including adjustment factors (e.g., scaling factors) that modify the dosage amount. Various aspects of the present disclosure relate to methods for determining adjustment parameters by simulating a person's glucose levels under different dosage scenarios (including different rates of change). In addition to the adjustment factor, the adjustment parameter may also include a threshold associated with the condition to which the adjustment is to be applied. For example, a threshold ROC may be defined as triggering an adjustment when the threshold ROC is exceeded. Based on the results of the simulation, the adjustment factor may be determined together with the threshold. In addition, the simulation may be performed in advance before obtaining the measurement that causes the application of the adjustment. In some specific implementations, the simulation is performed by a computing system 130, which may be able to access more computing resources (processing power, memory, bandwidth, etc.) than the monitoring device 110. In this way, adjustments can be applied when dosing without imposing a significant computing burden on the monitoring device 110.

[0036] Regardless of which component in the system 100 determines the adjustment factor, the monitoring device 110 may store the adjustment factor in the memory subsystem 118 for subsequent use with respect to the person 105. The monitoring device 110 may store the adjustment factor along with the aforementioned threshold value. The threshold value and the adjustment factor may together form adjustment parameters that define one or more rules evaluated by the monitoring device 110 to determine when to apply an adjustment and the extent of the dose adjustment. Such rule-based adjustments are typically performed for bolus doses, but may also be applied to basal doses when the conditions in the rules are met.

[0037] The delivery device 120 is configured to deliver a dose of a therapeutic agent under the control of the monitoring device 110. For example, the delivery device 120 can be an insulin pump that delivers insulin from a reservoir subcutaneously or percutaneously into the body of the person 105 using a needle, cannula, or other device. Therefore, the delivery device 120 can be communicatively coupled to the monitoring device 110 to receive dosing information indicating when a dose is delivered and the amount of the dose. In some specific implementations, the monitoring device 110 can schedule a dose in advance by sending a timestamp indicating the delivery time to the delivery device 120. Alternatively or additionally, the monitoring device 110 can output a control signal that activates the delivery device 120 when a dose is administered.

[0038] To indicate a dose amount (e.g., the number of units of insulin), the monitoring device 110 may transmit an adjusted amount corresponding to the initial calculated amount with the adjustment factor applied. Thus, the delivery device 120 may receive the final value of the dose. Another way that the monitoring device 110 may indicate a dose amount is to transmit the initial calculated amount and the adjustment factor so that the delivery device 120 can calculate the adjusted amount.

[0039] In some examples, the delivery device 120 is a manually operated device. For example, the delivery device 120 can be a reusable injection pen equipped with a communication interface (e.g., a wireless transceiver) for communicating with the monitoring device 110 and / or another component in the system 100. When operated by a user (e.g., a user of the monitoring device 110 and / or a person 105 receiving a dose), the injection pen can transmit the amount of drug delivered to help the monitoring device 110 track the dose.

[0040] The computing system 130 includes one or more processing units 132 and a memory subsystem 134. In some instances, the computing system 130 may be a server computer or a cloud computing network with multiple servers. The processing unit 114 and the memory subsystem 134 may be configured similarly to the processing unit 114 and the memory subsystem 118 of the monitoring device. However, as described above, the computing system 130 may be able to access more computing resources. For example, the computing system 130 may have a greater number of processors, a faster processor, and / or a larger storage capacity. In addition, the computing system 130 may be configured to perform certain operations in parallel. For example, a simulation for determining an adjustment parameter may be performed across a group of cloud servers, each of which runs a simulation across a different search space. Therefore, the first cloud server may determine the best adjustment factor and the corresponding threshold for the first group of scenarios, the second cloud server may determine the best adjustment factor and the corresponding threshold for the second group of scenarios, and the results may be combined by the first cloud server, the second cloud server, or the third cloud server to make a final determination of the adjustment parameter.

[0041] The computing system 130 also includes a simulation module 136 and one or more physiological models 138. The simulation module 136 is configured to perform simulations using the physiological models 138. The physiological models 138 may include at least one glucose model that describes the glucose response of the person 105 under different scenarios. The computing system 130 may store models of other persons besides the person 105. Thus, the computing system 130 may determine personalized adjustment parameters for multiple persons (e.g., a group of patients) and transmit these adjustment parameters to their corresponding monitoring devices.

[0042] The simulation module 136 may be implemented in hardware and / or software. For example, the simulation module 136 may be formed using the processing unit 132. In some specific implementations, the simulation module 136 includes a program code that includes instructions that cause the processing unit 132 to perform simulations and / or perform related operations. Therefore, the simulation module 136 may correspond to a software program executed as part of configuring the monitoring device 110 to perform dose adjustments. During the life of the monitoring device, the adjustment parameters may be repeatedly updated and transmitted to the monitoring device 110. For example, the computing system 130 may periodically rerun the simulation using new measurements (e.g., once a month) to determine whether the adjustment parameters can be improved by changing one or more values. If so, the computing system 130 may transmit the updated adjustment parameters to reconfigure the monitoring device 110.

[0043] Physiological model 138 may include any number of predictive models suitable for estimating the physiological condition of person 105. In the case of glucose levels, a variety of glucose models exist that differ in modeling approach and model complexity. Figure 2 (discussed below) shows a simplified representation of an exemplary glucose model. Typically, a glucose model is configured to output an estimate of glucose levels at a specific point in time or for a specific time period.

[0044] Traditional glucose model is a mathematical model based on the deterministic equation that characterizes glucose-insulin response. Glucose model can be applied to predict someone's glucose level in the near future (e.g., in the next half hour), and based on the current situation (e.g., current glucose level) and further based on the understanding of the activity that affects glucose (such as eating or exercising) to assess how their glucose level is expected to change. Therefore, the input of glucose model can include glucose measurement (e.g., one or more SG values) and insulin dosage information (e.g., the number of units of insulin and delivery time) etc. However, the technology described herein can be applied to models of other physiological conditions. In addition, the described technology is not limited to mathematical models, but can also be used with other types of models, which can be non-deterministic (e.g., statistical / probability models) and / or based on machine learning (e.g., neural network models).

[0045] In some embodiments, simulations using a person-specific model may be performed by another component besides computing system 130. For example, computing system 130 may use a relatively large number of historical measurements performed on person 105 to determine the tuning parameters. The historical measurements may include measurements obtained from sensor 112 and / or other sensors (e.g., sensor 112 in a previous instance of monitoring device 110). After being configured according to the tuning parameters, monitoring device 110 may periodically rerun the simulation using a smaller set of measurements (e.g., measurements obtained only after the initial configuration) and / or within a narrower search space (e.g., within a certain range of values ​​for the tuning parameters). As described below in conjunction with Fig.13 As discussed, such a rerun of the simulation may be performed to cover adjustments without having to update the adjustment parameters. Thus, the physiological model 138 may be shared across multiple computing systems or devices. For example, the monitoring device 110 may have a generic physiological model, and the computing system 130 may be responsible for customizing the model based on model parameters specific to the person 105. After obtaining the model parameters (e.g., through calculation and / or user input), the computing system 130 may transmit the model parameters to the monitoring device 110 for use in simulations or other purposes.

[0046] Figure 2 An example of a glucose model 200 is shown, which may be used to generate a glucose estimate 215 as part of performing a simulation. The glucose model 200 may correspond to Figure 1 1. The physiological model 138 in FIG. 1. The glucose model 200 includes three components or compartments: a glucose component 210, an insulin component 220, and a meal component 230. Each component of the glucose model 200 has one or more inputs associated therewith. The inputs to the glucose component 210 include a glucose measurement 202, which may be a BG and / or SG value from a sensor such as the sensor 112. The inputs to the insulin component 220 include insulin dosage information 204, such as units of insulin. The inputs to the meal component include meal information 206, such as the number of carbohydrates, as indicated in Equation 1 above.

[0047] The glucose component 210 describes glucose kinetics and can be characterized by the following equation: (2) (3) Equation 2 describes glucose in the bloodstream as equal to the sum of glucose naturally produced in the body and glucose contributed by meals, minus glucose reduced by insulin action, minus glucose diffused from plasma into the interstitial fluid. Equation 3 describes subcutaneous (e.g., interstitial) glucose as equal to glucose diffused from the blood minus glucose taken up by surrounding subcutaneous (e.g., fat or muscle) cells. G is the blood glucose level. It is the glucose clearance rate that is independent of insulin. SI is similar to Equation 1 ISF , but can be expressed in different units, such as (dL / min) / (µU / mL). Stands for plasma insulin, and is the amount of insulin in the blood (indicated by the insulin component). is the rate at which carbohydrates are absorbed. It is the rate at which glucose appears in the blood after a meal (as indicated by the meal component). It's weight. is the volume of distribution of glucose and is proportional to body weight. is the concentration of endogenously produced glucose. Represents subcutaneous glucose, such as the concentration of glucose in the interstitial fluid. is the rate at which glucose is absorbed into the subcutaneous tissue.

[0048] The insulin component 220 describes the insulin kinetics and can be characterized by the following equation: (4) (5) Equation 4 describes subcutaneous insulin as equal to insulin delivered and subsequently absorbed minus insulin diffused into the blood. Equation 5 describes insulin in the blood as equal to insulin from the interstitial fluid minus insulin cleared through insulin metabolism. Represents subcutaneous insulin, ie, the concentration of insulin in the interstitial fluid. It is a parameter that indicates the speed of subcutaneous insulin absorption. J represents the amount of insulin delivered over time. is the plasma insulin level. It is a parameter that indicates the rate of plasma insulin absorption.

[0049] The meal component 230 describes the glucose contribution of the meal and can be characterized by the following equation: (6) (7) Equation 6 describes the glucose contribution of a meal as equal to the glucose converted from carbohydrates minus the glucose absorbed into the bloodstream from the digestive system. Equation 7 converts the rate at which glucose appears in the blood (in Equation 2) to ) is described as equal to the glucose from the digestive system minus the glucose present in the blood due to meals. M represents the amount of glucose available from meals. is the carbohydrate absorption rate. It is the carbohydrate-glucose conversion factor. meal ( t ) describes carbohydrate intake over time.

[0050] Based on the discussion of equations 2 to 7 above, it can be appreciated that some of the parameters in the glucose model 200 are person-specific and / or time-varying (e.g., body weight or insulin sensitivity index). Figure 1 , glucose model 200 may include model parameters 240 stored in association with person 105. Model parameters 240 may be obtained in various ways, including, for example, through user input to monitoring device 110 or computing system 130. In some instances, one or more model parameters may be calculated, for example, as part of fitting a physiological model to measurements or other data associated with person 105.

[0051] supply Figure 2 105 , 106 , 107 , 108 , 109 , 200 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 47 , 48 , 49 , 50 , 65 , 66 , 67 , 68 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82

[0052] Figure 3 An example of a memory subsystem 300 for storing adjustment-related data is shown. The memory subsystem 300 may correspond to the memory subsystem 118 of the monitoring device 110 and / or the memory subsystem 134 of the computing system 130. Figure 3 As shown, memory subsystem 300 may store model parameters 302, adjustment parameters 304, and measurements 306. The contents of memory subsystem 300 may be stored in a centralized or distributed manner and using any number of data structures. For example, if memory subsystem 300 is Figure 1 If the memory subsystem 118 in FIG. 1 is used, the model parameters 302 and the measurements 306 may be stored external to the processing unit 114 , while the adjustment parameters 304 may be stored in a processor-level cache and / or hardware registers for faster access by the processing unit 114 .

[0053] Model parameters 302 may include parameters used by physiological model 138 to estimate the value of glucose level or another physiological condition. For example, model parameters 302 may include values ​​for any of the person-specific variables in equations 2 through 6 above. Model parameters 302 may also include parameters used to calculate the initial dose (e.g., the value of the initial dose in equation 1). ICR and ISF ). Thus, the model parameters 302 may be accessed in conjunction with performing a simulation to determine the adjustment parameters 304 and / or in conjunction with determining the size of a dose.

[0054] The adjustment parameters 304 are parameters used to adjust the dosage amount. As previously discussed, the adjustment parameters may include an adjustment factor and a threshold value associated with the conditions under which the adjustment is to be applied. Figure 4 An example is shown of how the adjustment parameters 304 may be arranged. However, other specific implementations of the adjustment parameters are possible.

[0055] Measurements 306 may include measurements performed on person 105. Measurements 306 may use one or more sensors and be collected over any time period. For example, measurements used to determine adjustment parameters 304 may be collected over a period of one week or longer. The number of measurements collected depends on the sampling frequency. For example, a CGM device may obtain measurements at intervals of 1 minute, 5 minutes, or 15 minutes. Measurements 306 may include interstitial glucose readings (e.g., SG values ​​from an interstitial glucose sensor). In some instances, measurements 306 may include reference values ​​(e.g., BG values ​​from a blood glucose meter). BG values ​​are often collected less frequently, so when both SG and BG are collected, SG values ​​are typically higher than BG values. Measurements 306 may also include measurements collected after adjustment parameters 304 have been determined, such as measurements used to calculate a dose according to equation 1. The number of measurements considered for dosing purposes is typically significantly less than the number of measurements used to determine adjustment parameters 304.

[0056] Figure 4 An example of adjustment parameters arranged into parameter sets is shown. Figure 4In [the context], the adjustment parameter 304 includes a first parameter set 410 and a second parameter set 412. Each parameter set includes an adjustment factor and a corresponding threshold. The threshold specifies the condition under which the adjustment factor will be applied to modify the dose. For example, the first parameter set 410 may include parameters applicable when the glucose level is on an upward trend. Thus, the first parameter set 410 may include an adjustment factor 402 and a measurement threshold 404, which represents the lowest glucose value at which the adjustment factor 402 will be applied. The first parameter set 410 may also include an ROC threshold 406, which represents the lowest glucose rate of change at which the adjustment factor 402 will be applied. Thus, the use of the adjustment factor 402 will be conditional on the measured glucose (BG or SG) being higher than the measurement threshold 404 and the glucose ROC being higher than the ROC threshold 406. Thus, the first parameter set 410 may be represented by the following logical rule: If the measured glucose > measurement threshold and the glucose ROC > ROC threshold, then increase the dose according to the adjustment factor.

[0057] Similarly, the second parameter set 412 may include a separate adjustment factor 402, a corresponding measurement threshold 404, and a corresponding ROC threshold 406, which are applicable when the glucose level is on a downward trend. The measurement threshold 404 of the second parameter set 412 may represent the highest glucose value at which the adjustment factor 402 of the second parameter set 412 will be applied. The ROC threshold 406 of the second parameter set 412 may represent the highest glucose rate of change at which the adjustment factor 402 of the second parameter set 412 will be applied. Thus, the use of the adjustment factor in the second parameter set 412 will be conditional on the measured glucose being lower than the measurement threshold 404 and the glucose ROC being lower than the ROC threshold 406. Thus, the second parameter set 412 may be represented by the following logical rule: If the measured glucose < measurement threshold and the glucose ROC < ROC threshold, then decrease the dose according to the adjustment factor.

[0058] The adjustment factor 402 may be configured to provide an increase or decrease in the calculated dose depending on which threshold conditions are met. In the case of an upward trend, a rapidly increasing glucose level (characterized by a positive ROC value) typically requires an increase in insulin. Thus, the adjustment factor 402 of the first parameter set 410 may be a scaling factor that needs to be multiplied by the calculated dose, where the scaling factor is a value greater than 1. Conversely, in the case of a downward trend, a rapidly decreasing glucose level (characterized by a negative ROC value) typically requires a decrease in insulin. Thus, the adjustment factor 402 of the second parameter set 412 may be a scaling factor with a value less than 1. The adjustment factors in the parameter sets 410 and 412 may modify the dose to different extents. For example, the parameter set 410 may provide a 60% increase using a scaling factor of 1.6, while the parameter set 412 may provide a 50% decrease using a scaling factor of 0.5.

[0059] Provide Figure 4As an example of how thresholds related to the rate of change in physiological conditions may be used to specify circumstances in which dosage is to be adjusted. Figure 4 In the example of , each parameter set includes a measurement threshold value combined with a ROC threshold value. However, the number of threshold values ​​in a parameter set may vary. For example, in some embodiments, an upper and lower limit of the ROC range may be specified instead of a single ROC threshold value 406. The total number of parameter sets may also vary. For example, instead of defining a single parameter set for all rising trend situations, the first parameter set 410 may be divided into multiple parameter sets, each with its own adjustment factor and corresponding threshold value. Similarly, the second parameter set 412 may be divided into multiple parameter sets so that the adjustment factor varies between different falling trend situations.

[0060] Figure 5A 500 is an exemplary graph of glucose high excursion rate and glucose ROC during bolus injection. High excursion refers to a rise in glucose to a level associated with hyperglycemia (e.g., above 180 mg / dL). Graph 500 includes five pairs of plotted curves labeled A to E. Each pair of plotted curves covers a different glucose range, including a first plotted curve of the ROC calculated using SG values ​​from an interstitial glucose sensor, and also including a second plotted curve of the ROC calculated using BG values. As indicated by the interval symbols in the figure (square brackets indicate included, round brackets indicate excluded), A covers glucose levels of [70–120), B covers [120–180), C covers [180-240), D covers [240-300), and E covers [300-350).

[0061] Figure 5A It is shown that bolus doses taken when glucose is rising rapidly often result in hyperglycemia. For example, region 502 corresponds to the rapid onset of hyperglycemia when a bolus dose is given at a highly positive ROC, even though glucose is below 180 mg / dL at the time of dosing. The dose given in conjunction with the measurements reflected in region 502 is insufficient to offset the rising glucose level. In this case, the dose may be increased to prevent or reduce the onset of hyperglycemia.

[0062] Figure 5B 500 is an exemplary graph of glucose low excursion rate and glucose ROC during a bolus injection. A low excursion refers to a drop in glucose to a level associated with hypoglycemia (e.g., below 70 mg / dL). Graph 510 includes five pairs of plotted curves, which are labeled similarly to graph 500. Figure 5BIt is shown that bolus doses given when glucose is changing rapidly often result in hypoglycemia, regardless of the direction of the change (upward or downward). For example, region 505 corresponds to a rapid onset of hypoglycemia when a bolus dose is given at a highly negative ROC, and region 507 corresponds to a rapid onset of hypoglycemia when a bolus dose is given at a highly positive ROC. The dose given in combination with the measurements reflected in regions 505 and 507 is excessive relative to the patient's actual insulin needs, even if glucose is 70 mg / dL or higher at the time of dosing. In this case, the dose may be reduced to prevent or reduce the occurrence of hypoglycemia.

[0063] solve Figure 5A and Figure 5B One potential solution to the illustrated problem is to adopt a universal one-size-fits-all approach for everyone. Under the universal approach, insulin delivery may be adjusted by a predetermined amount (e.g., a fixed percentage) when glucose levels change by more than a threshold amount. For example, when the magnitude of the ROC exceeds a first threshold in a positive direction, the dose may be increased by 10%, and when the magnitude of the ROC exceeds the first threshold in a negative direction, the dose may be decreased by 10%. However, this universal approach still results in inaccurate dosing because it does not take into account lifestyle and physiological differences between people.

[0064] Figure 6 600 is a graph of the average change in time above range (TAR) and the average change in time below range (TBR) for a sample patient population when the same set of adjustment parameters are applied across the entire patient population. Graph 600 shows that there is a tradeoff between TAR and TBR across populations. Doses that reduce TBR tend to increase TAR. Similarly, doses that reduce TAR tend to increase TBR. There is no dose that minimizes both TAR and TBR for all people. Therefore, the general approach is generally conservative in terms of the degree of adjustment and the conditions under which the adjustment is made. For example, the frequency of application of adjustments may not be high (e.g., ten times in a year or more), and the changes in the amount of dose involved may be less than the amount of change that is optimal for the recipient.

[0065] When glucose levels change rapidly, a person-specific approach may be applied to adjust the dose (e.g., bolus dose) rather than a generic approach. As described above, physiological model 138 may include one or more models specific to the dose recipient (e.g., person 105) and may be used to determine a set of adjustment parameters by simulating glucose levels under different scenarios. The simulated scenarios may include different starting glucose values ​​(e.g., SG at the time of administration), different rates of change, different dose amounts, and / or other factors that affect the recipient's glucose levels.

[0066] Figure 7700 is a graph of time in range (TIR) ​​and sample index for fifty measurement samples from a representative diabetic patient. Each measurement sample corresponds to a separate dose. Graph 700 shows the increase in TIR resulting from applying person-specific adjustments using adjustment parameters derived according to the techniques described herein. For the purposes of this discussion, TIR may refer to the duration of time that glucose levels are normal (e.g., 70 mg / dL to 180 mg / dL, and particularly 100 mg / dL to 120 mg / dL). However, TIR can be the time spent within any target range. The boundaries of the target range depend on the physiological condition in question. Figure 7 In the example of , applying a person-specific adjustment resulted in an increase in the mean TIR of approximately 3% compared to delivering an unadjusted dose.

[0067] The results in graph 700 indicate that TIR is a suitable metric for evaluating potential adjustment parameter values. Therefore, in some implementations, TIR is used as a selection criterion to determine the adjustment parameters. For example, candidate parameter sets may be formed, each of which includes a different combination of adjustment factors, measurement thresholds, and ROC thresholds. Each candidate parameter set may then be evaluated by simulation to determine whether the candidate parameter set results in an increase in TIR without increasing the time below range (TBR). Any candidate parameter set that results in an increase in TBR may be excluded from further consideration. However, some implementations may allow the TBR to increase by a certain amount as long as the TIR increases. For example, before running a simulation to determine parameter sets 410 and 412 for person 105, the computing system 130 may request user input to confirm whether the increased TBR is acceptable to person 105. Other selection criteria or optimization strategies may be used to determine which adjustment parameter values ​​to use for any particular person.

[0068] Figure 8 Included are three graphs (810, 820, and 830) showing the Figure 7 The tuning parameter values ​​of the tuning parameter set in . For simplicity, Figure 8 The examples are limited to uptrend parameters, such as Figure 4 410 in the parameter set. However, similar techniques can be used to determine the downward trend parameters. In this example, the adjustment parameters are initially divided into 74 candidate parameter sets and simulated to determine that 51 of the 74 candidate parameter sets (shaded areas) result in an increase in TIR without an increase in TBR. The remaining 23 parameter sets either do not increase TIR or increase TBR while increasing TIR and are therefore excluded from consideration. The final selected adjustment parameters include measurement threshold 801, ROC threshold 802, and adjustment factor 803.

[0069] The simulation is performed using a glucose model derived from measurements performed on a representative diabetic patient. For example, the SG and / or BG values ​​are recorded together with the corresponding insulin dose given at the time of the glucose measurement. The parameters of the glucose model (e.g., the value of SI and the above parameters for Figure 2 The glucose model is then used to simulate glucose levels for different combinations of adjustment parameter values ​​(measurement threshold, ROC threshold, and adjustment factor). Each combination represents a candidate parameter set that is simulated to determine the corresponding TIR and TBR. The results are then compared to determine which candidate parameter sets meet the criteria of increasing TIR without increasing TBR.

[0070] Graph 810 shows a distribution of candidate glucose level values ​​that form measurement threshold 801. The range of glucose levels is 100 mg / dL to 250 mg / dL. Measurement threshold 801 has a value of 125 mg / dL and is determined to be the median value within glucose level group 812 associated with a candidate parameter set that meets the above criteria.

[0071] Graph 820 shows the distribution of candidate ROC values ​​that form ROC threshold 802. The range of ROC values ​​is 0.2 mg / dL to 1.0 mg / dL per minute. ROC threshold 802 has a value of 0.25 mg / dL / min and is determined as the median value within the ROC value group 814 associated with the candidate parameter set that meets the above criteria.

[0072] Graph 830 shows a distribution of candidate adjustment factor values ​​that form adjustment factor 803. The adjustment factor values ​​correspond to scaling factors (1.1 to 1.9) that provide an increase between 10% and 90%. However, the adjustment factor is not limited to a scaling factor and may be any value that modifies the dose amount. For example, the adjustment factor may be an offset value that is added to the initial dose. Adjustment factor 803 has a value of 1.2 (corresponding to an increase of 20%) and is determined to be the median value within the group of adjustment factor values ​​816 associated with the candidate parameter set that meets the above criteria.

[0073] Although the measurement threshold 801, the ROC threshold 802, and the adjustment factor 803 are calculated as medians, other methods of determining the adjustment parameter values ​​are possible. For example, in some implementations, the adjustment parameter may be calculated as an average. As another example, instead of considering each adjustment parameter individually, a set of candidate parameters may be plotted together (e.g., as three-dimensional points) within the entire search space to calculate a centroid with coordinates corresponding to the measurement threshold 801, the ROC threshold 802, and the adjustment factor 803.

[0074] Figure 8This is merely an example of how a set of adjustment parameters may be determined based on simulation results. The values ​​discussed in conjunction with this example are illustrative and not limiting. Thus, the search space may vary depending on the specific implementation and / or the physiological conditions in question. For example, in some implementations, the search space for adjustment parameters applied to insulin dosage may cover the following range of values: Measurement (glucose) threshold: 100mg / dL to 350mg / dL (upward trend) or 40mg / dL to 150mg / dL (downward trend), ROC thresholds: 0.25 mg / dL / min to 2.5 mg / dL / min (upward trend) or -0.25 mg / dL / min to -2.5 mg / dL / min (downward trend), and Adjustment factor: 5% to 90% increase (upward trend) or 5% to 90% decrease (downward trend).

[0075] Fig.9A and Fig. 9B Exemplary results of adjusting insulin dosage are shown. Fig.9A is a comparison between the glucose plot 902 without adjustment and the glucose plot 904 with adjustment 900 applied. Fig. 9B 9 is a bar graph showing the difference between the amount of the unadjusted dose 912 and the amount of the adjusted dose 914 corresponding to the adjustment 900. Fig.9A As indicated, the glucose response was essentially the same prior to adjustment 900, as earlier doses 903 and 905 were not adjusted. Adjustment 900 was applied based on a measurement threshold of 100 mg / dL, a ROC threshold of 0.25, and an adjustment factor of 1.55 (55% increase).

[0076] Adjustment 900 increases dose 912 by approximately one unit and is applied when glucose is rising rapidly (i.e., ROC is above 0.25). Adjusted dose 914 results in a return to target range 920. In this example, target range 920 is set to have a lower threshold (LT) of 70 mg / dL and an upper threshold (UT) of 180 mg / dL, respectively. Return to target range 920 occurs after subsequent dose 907, which is not adjusted because the threshold conditions for adjustment 900 do not exist at the time of subsequent dose 907. In contrast, plotted curve 902 shows that glucose levels fail to return to target range 920 even after subsequent dose 907. Without adjustment, the TIR across the entire measurement period is 57.3, while with adjustment 900, the TIR is 68.6. Thus, this example shows that adjustments can be applied to facilitate a return to a target range and / or increase time in range.

[0077] Fig. 10A and Fig. 10BAnother example of the results of adjusting insulin dosage is shown. Fig. 10A 1004 is a comparison between a glucose plot 1002 without adjustment and a glucose plot 1004 with adjustment 1000 applied. Fig. 10B 1000 is a bar graph showing the difference between the amount of the unadjusted dose 1012 and the amount of the adjusted dose 1014 corresponding to the adjustment 1000. Fig. 10A As indicated, the glucose response was essentially the same before adjustment 1000 because earlier doses 1003 and 1005 were not adjusted. Adjustment 1000 was applied based on a measurement threshold of 160 mg / dL, a ROC threshold of 0.75, and an adjustment factor of 1.60 (a 60% increase). Adjustment 1000 increased dose 1012 by approximately 2 units and was applied when glucose was rising rapidly (i.e., ROC was above 0.75). Fig.9A and Fig. 9B In contrast to the example in , here the glucose level would eventually return to the target range 920 even without the adjustment. However, as indicated by plotted curve 1004, the adjustment 1000 results in a faster return to the target range 920.

[0078] In addition, adjustment 1000 enables the subsequent dose 1007 to be delivered using less insulin. Specifically, the subsequent dose 1007 involves an insulin amount 1018 that is lower than the corresponding original amount 1016. However, the insulin amount 1018 is not calculated by applying an adjustment factor. Instead, both amounts 1016 and 1018 are determined using the same dosing formula and based on the conditions prevailing at the time of the subsequent dose 1007. The difference between the original amount 1016 and the insulin amount 1018 is because: in the adjusted case (plotted curve 1004), by the time of the subsequent dose 1007, the glucose level has dropped to the target range 920, while in the unadjusted case (plotted curve 1002), the glucose is still outside the range. Therefore, the adjusted dose can affect the amount of the subsequent dose even if the adjustment factor is not applied to the subsequent dose.

[0079] Fig.11 is a flow diagram of a process 1100 for adjusting dosage according to some embodiments. Fig.11 The processes depicted in the flowcharts and subsequent flowcharts are described with respect to insulin dosage and glucose levels, but may also be applied to drug delivery in other therapeutic settings. Process 1100 may be performed on a computing device that has access to one or more sets of adjustment parameters. For example, process 1100 may involve a computer such as Figure 1 The operations performed by the processing unit of the monitoring device 110 in the monitoring device.

[0080] At 1102, a glucose value and a glucose ROC are obtained based on a glucose measurement performed on a person (e.g., person 105). The glucose value may correspond to a most recent measurement of the person's glucose level. The glucose ROC may be determined by two or more recent measurements. For example, the glucose ROC may be calculated by subtracting the value of the previous measurement from the value of the most recent measurement and dividing the difference by the time interval between the measurements. Other methods of determining the ROC are also possible, including, for example, determining it as an average rate of change of three or more consecutive measurements. Glucose measurements are typically performed using an interstitial glucose sensor, but may include measurements from other types of sensors (e.g., a blood glucose meter).

[0081] At 1104, an initial amount of insulin to be delivered to the person is calculated based on the glucose value obtained at 1102. The initial amount of insulin may be calculated based on a dosing formula such as Equation 1. The dosing formula may utilize the glucose value from 1102 in combination with ISF, ICR, and other factors to calculate the initial amount of insulin, but typically does not take into account ROC.

[0082] At 1106, it is determined that the glucose value exceeds the glucose threshold and that the glucose ROC exceeds the ROC threshold. The glucose threshold and the ROC threshold may be specified as part of the same set of adjustment parameters and may correspond, for example, to Figure 4 The measurement threshold 404 and the ROC threshold 406 in the parameter set 410 or the parameter set 412.

[0083] At 1108, an adjustment value is obtained in response to the determination in 1106. The adjustment value represents the adjustment to be applied to the initial insulin amount when the glucose threshold and the ROC threshold are exceeded. For example, the adjustment value may correspond to the adjustment factor 402 from the same parameter set as the glucose threshold and the ROC threshold. The adjustment value provides an increase or decrease in the initial insulin amount and can be represented as a proportional factor. In contrast to the initial insulin amount, the adjustment value is predetermined based on an earlier glucose measurement performed on the same person (i.e., before the measurement in 1102). The earlier glucose measurement may be performed using the same or different sensors. For example, an earlier instance of the monitoring device that obtains the measurement at 1102 may have recorded an earlier glucose measurement within a week or two weeks. The adjustment value may be retrieved from a memory associated with the computing device that performs the process 1100, such as the memory subsystem 118 of the monitoring device 110. The adjustment value may have been stored in association with the glucose threshold and the ROC threshold from 1106, such as as part of the same adjustment parameter set.

[0084] At 1110, the initial insulin amount is adjusted according to the adjustment value. The insulin dose may be delivered automatically by an insulin delivery device (e.g., delivery device 120) or manually by a user of the insulin delivery device (e.g., person 105). The adjustment may involve transmitting the adjusted insulin amount to the insulin delivery device or user, resulting in delivery of the adjusted insulin amount instead of the initial insulin amount. In some examples, the initial insulin amount may also be transmitted.

[0085] Fig.12 1 is a flow chart of a process 1200 for determining adjustment parameters according to some embodiments. The process 1200 may be performed on a computing device or system that has access to historical glucose measurements. For example, the process 1200 may involve a process such as Figure 1 Operations performed by a processing unit of a computing system in computing system 130 .

[0086] At 1202, glucose measurements and information about glucose-related events are obtained. Glucose measurements may be performed by one or more sensors (e.g., an interstitial glucose sensor and / or a blood glucose meter) and may be collected over a period of days, weeks, or months. Thus, glucose measurements may include SG and / or BG values ​​obtained from one or more sensors. For example, computing system 130 may receive glucose measurements and information about glucose-related events from one or more monitoring devices that collect measurements from sensors used on a person. Glucose-related events may include insulin doses (e.g., delivery time and dose amount), meals (e.g., meal times and the number of carbohydrates in the meal), and other events that affect glucose levels.

[0087] At 1204, a physiological model is derived from the glucose measurements and event information obtained at 1202 as a person-specific model that can be used to estimate glucose levels. In particular, the person-specific model can be configured to estimate the person's glucose level at a specific time in the future based on information about the person's current state (e.g., current glucose level and glucose ROC) and further based on information about glucose-related events that have occurred or are expected to occur (e.g., an upcoming basal dose). This derivation can involve fitting a general physiological model and can result in customizing one or more model parameters based on the person's unique physiology and behavior so that the glucose estimate output by the person-specific model is consistent with the glucose measurements and event information from 1202.

[0088] At 1206, candidate parameter sets are generated using different possible combinations of glucose thresholds, ROC thresholds, and adjustment values. For example, each candidate parameter set may include values ​​for adjustment factor 402, measurement threshold 404, and ROC threshold 406, and each candidate parameter set may differ from each other candidate parameter set in terms of the value of at least one of these three parameters. Thus, each candidate parameter set may include a unique combination of parameter values ​​that represent conditions for which adjustment may be applied. Candidate parameter sets may be randomly generated. Back to Reference Figure 8 In the example discussed, one or more candidate parameter sets may be formed to include a random measurement threshold between 100 mg / dL and 350 mg / dL, a random ROC threshold between 0.25 and 2.5, and / or a random adjustment factor between 5% and 90%. Alternatively or additionally, candidate parameter sets may be generated as a change in observation conditions. For example, the measurement and event information from 1202 may indicate a glucose value and a glucose ROC at an actual dose and a dose amount. One or more candidate parameter sets may then be generated by changing the glucose value, the glucose ROC, and / or the dose amount to form a hypothetical scenario. Multiple candidate parameter sets may be generated from observations about a single dose.

[0089] At 1208, a human-specific model is applied to simulate the glucose level of a person under different dosage scenarios. In particular, the candidate parameter set from 1206 is input to the human-specific model to estimate the glucose response of a person to an insulin dose delivered using different dosage amounts and at different glucose levels and different rates of change. The simulated dose amount can be calculated based on the adjustment value in the candidate parameter set, such as calculating the bolus dose according to equation 1 and modifying it according to the value of the adjustment factor 402. The estimated glucose level can cover a certain duration after each simulated dose is delivered, such as the next 30 minutes or until the next expected dose. In this way, the dosage scenario can be evaluated based on TIR, TAR, TBR and / or other metrics related to the change of glucose over time. In addition, in some instances, the duration can span multiple doses, including a simulated dose and one or more subsequent doses.

[0090] At 1210, candidate parameter sets that are not associated with an increase in TIR or are associated with an increase in TBR are eliminated, leaving a subset of the remaining candidate parameter sets. Thus, only dose scenarios associated with improved outcomes relative to delivering an unadjusted dose under the same circumstances may be considered.

[0091] At 1212, the value of the adjustment parameter set (eg, parameter set 410 or parameter set 412) is determined based on the remaining candidate parameter sets. The value of the adjustment parameter set can be determined in various ways, such as by calculating each adjustment parameter as a median or average value, as described above in conjunction with Figure 8As discussed. The set of adjustment parameters determined at 1212 provides personalized dosing and can be used to determine a more optimal dose in various dosing scenarios. Based on the results of the simulation in 1208, whenever the glucose threshold and the ROC threshold are exceeded at the same time, the adjusted insulin amount is expected to maximize the time in range relative to other (e.g., unadjusted) insulin amounts. When the glucose threshold or the ROC threshold is not exceeded, the dose can be delivered without adjustment. Therefore, a combination of the adjusted dose and the unadjusted dose can be used to meet the insulin needs of the person.

[0092] Fig.13 1 is a flow diagram of a process 1300 for overriding an adjustment according to some embodiments. The process 1300 may be performed on a computing device or system that has access to one or more sets of adjustment parameters. For example, the process 1300 may involve operations performed by a processing unit of a monitoring device (e.g., monitoring device 110) after receiving the adjustment parameters 304 from the computing system 130. The process 1300 involves a safety check on the adjustment and may be performed to deliver the initial insulin amount instead of the adjusted insulin amount when it is determined that the adjusted insulin amount will result in a worse outcome than the initial insulin amount.

[0093] At 1302, a glucose value and a glucose ROC are obtained based on a glucose measurement performed on a person (eg, person 105). The functionality in 1302 may be similar to Fig.11 1102 is implemented in a similar manner.

[0094] At 1304, an initial amount of insulin to be delivered to the person is calculated based on the glucose value obtained at 1302. The calculation in 1304 may be similar to Fig.11 The calculations in 1104 are performed in a similar manner, for example using a dosing formula.

[0095] At 1306, an adjusted insulin amount is determined. The adjusted insulin amount represents an increase or decrease from the initial insulin amount calculated at 1304 and may be determined using an adjustment parameter set applicable to the glucose value and the glucose ROC obtained at 1302. For example, the adjusted insulin amount may be determined by applying an adjustment value based on a glucose value exceeding a glucose threshold and further based on a glucose ROC exceeding a ROC threshold, as described above with reference to Fig.11 1106 and 1108. However, process 1300 can be used to cover any planned adjustments to the dosage, regardless of how the adjusted amount is determined.

[0096] At 1308, a person-specific physiological model is used to simulate future glucose levels. The person-specific model may have been generated using earlier glucose measurements performed on the same person. The model used for the simulation in 1308 may be the same model used to determine the adjustment parameters for the adjusted insulin amount at 1306. For example, referring back to Fig.12 After deriving the model at 1204, computing system 130 may have transmitted the model parameters to monitoring device 110 (or another computing device in the system). Thus, monitoring device 110 or other computing device may be able to perform the same Fig.12 The simulation in 1208 is similar to the simulation in 1208.

[0097] The simulation in 1308 may have a more limited scope than the simulation used to determine the adjustment parameters. For example, instead of simulating glucose levels under various possible dosage scenarios, the simulation in 1308 may be limited to predicting the glucose changes that will occur after the adjusted insulin amount in 1306 is delivered. In addition, the simulation in 1308 may involve a shorter time frame (e.g., glucose levels within the next 30 minutes), while the simulation used to determine the adjustment parameters may involve a longer time frame (e.g., the next few hours or until the next dose). Therefore, the simulation in 1308 will generally focus on the current state of the person and the potential effects of the adjustment. Therefore, the glucose levels simulated in 1308 will include glucose levels that combine the glucose values ​​obtained in 1302 and the glucose ROC to represent the predicted results of delivering the adjusted insulin amount.

[0098] The simulation in 1308 may also differ in scope because it uses more recent information (e.g., the glucose measurements in 1302) that was not necessarily available at the time of the simulation used to determine the adjustment parameters. Furthermore, even if the earlier simulation covered the same conditions (e.g., the same glucose values ​​and glucose ROC as in 1302), the adjustment parameters will be determined across multiple dosing scenarios rather than being optimized for a single scenario. As discussed above with reference to Figure 8As discussed, the measurement threshold 801, the ROC threshold 802, and the adjustment factor 803 are determined based on a plurality of candidate parameter sets associated with an improved TIR. Therefore, although the adjusted insulin amount in 1306 is generally expected to result in a longer TIR compared to the initial insulin amount calculated in 1304, this is not always the case. For various reasons, sometimes the initial insulin amount performs better than the adjusted insulin amount. Changes in a person's physiology or behavior (e.g., weight gain, switching to a more sedentary lifestyle, missing meals) or incorrect user input (e.g., underestimating the number of carbohydrates in a meal) may result in dosing deviations, making the initial insulin amount actually more suitable for the next dose. Therefore, before delivering a dose in the adjusted amount, it may be beneficial to confirm that the adjusted insulin amount will work better.

[0099] At 1310, it is determined that one or more future glucose levels from the simulated future glucose levels in 1308 exceed a threshold value associated with a target glucose range. Future glucose levels may be evaluated relative to an upper threshold value (e.g., 180 mg / dL) and / or a lower threshold value (e.g., 70 mg / dL) of the target glucose range. For example, if the glucose value in 1302 is already close to the upper threshold value, drifts above the upper threshold value may be ignored. Similarly, if the glucose value in 1302 is close to the lower threshold value, drifts below the lower threshold value may be ignored. Therefore, high drift and / or low drift may be used as a heuristic method to conclude that the adjusted insulin amount will perform worse than the initial insulin amount.

[0100] The degree of rigor with which the determination in 1310 is made may vary. In some implementations, any drift (i.e., a single future glucose value) beyond the target glucose range may be sufficient to invalidate the adjusted insulin amount. Alternatively, the criteria may be relaxed by requiring a threshold number of future glucose values ​​to be beyond the target glucose range, such as ten drifts in the same direction. As another example, a distribution of drifts may be considered, such as to require ten drifts that are consecutive or across a certain size of time block / sample count.

[0101] Optionally, the simulation in 1308 may also involve predicting the results of delivering an initial insulin amount, thereby allowing a direct comparison between the initial amount and the adjusted amount (e.g., based on TIR). This involves more calculations, but increases the set of criteria that can be used to exclude the use of an adjusted insulin amount.

[0102] At 1312, in response to the determination in 1310, an initial insulin amount is delivered instead of the adjusted insulin amount. The delivery of the initial insulin amount can be achieved in a variety of ways. For example, the computing device making the determination in 1310 can postpone delivering the dose to the delivery device until the determination is complete. Alternatively, the computing device can deliver the initial insulin amount as a follow-up to delivering the adjusted insulin amount. In some implementations, the delivery device can receive an override command or signal indicating that the adjusted insulin amount will not be used.

[0103] The simulation in 1308 and the determination in 1310 may be repeated to make subsequent plan adjustments, for example, based on Fig.11 The operation shown adjusts the initial insulin amount at any time. The adjustments can be overridden one by one depending on the results of the determination in 1310. The overriding of the adjustments can be performed without updating or changing the existing adjustment parameters. As discussed above, the simulation in 1308 generally focuses on the current state of the person and the potential effects of the adjustments, rather than possible dosage scenarios. In this case, the updating of the adjustment parameters may not be appropriate.

[0104] Although the adjustment parameters may not be updated along with the adjusted overlay, the adjustment parameters may be occasionally updated to take into account newer measurements. For example, computing system 130 may periodically perform an update using glucose measurements and event information transmitted from monitoring device 110 to computing system 130. Fig.12 Then, each time computing system 130 determines new values ​​for the adjustment parameter set, computing system 130 may transmit these values ​​to monitoring device 110 for subsequent dosing.

[0105] The adjustment parameters may also be updated as needed in response to a user request. For example, when the monitoring device 110 receives user input of updated information about the person 105 (e.g., a new ISF or ICR value), the monitoring device 110 may prompt the user for additional input to indicate whether the adjustment parameters should be updated. If the user indicates that they want to update the adjustment parameters, the monitoring device 110 may request the computing system 130 to rerun the simulation in 1208. In some examples, the adjustment parameters may be updated in conjunction with an update to the physiological model, such as when the physiological model is updated based on the person's physical condition. Fig.12 The functionality of 1204 is refitted.

[0106] Fig.14 An exemplary therapy delivery system 1400 for a person 1401 is depicted. Similar to Figure 11400 can be an insulin delivery system. Thus, functionality or features described for components of system 100 can also be provided by similar components in system 1400. Likewise, components of system 100 can incorporate functionality or features described with respect to their counterparts in system 1400. However, any aspect, embodiment, or description intended to be related to an insulin delivery system should be applicable to a therapy delivery system that delivers therapies other than insulin.

[0107] like Fig.14 As shown, therapy delivery system 1400 includes a delivery device 1402, a monitoring device 1404, a computing device 1406, and an optional remote or cloud computing system 1408. Delivery device 1402 may correspond to Figure 1 1404. Monitoring device 1404 may correspond to monitoring device 110. Computing device 1406 may correspond to computing system 130 or a device in communication with computing system 130. For example, remote / cloud computing system 1408 may assume the role of computing system 130, and computing device 1406 may operate as a trusted intermediary to facilitate secure (e.g., encrypted) communications between computing system 1408 and other components such as delivery device 1402 and / or monitoring device 1404.

[0108] Delivery device 1402, monitoring device 1404, and computing device 1406 may be embodied in various ways, including being disposed in one or more device housings. For example, in some embodiments, all of devices 1402-1406 may be disposed in a single device housing. In some embodiments, each of devices 1402-1406 may be disposed in a separate device housing. In some embodiments, two or more of devices 1402-1406 may be disposed in the same device housing, and / or a single device 1402, 1404, or 1406 may have two or more portions disposed in two or more housings. Such embodiments and combinations thereof are contemplated to be within the scope of the present disclosure.

[0109] Fig.14 Communication links 1412-1418 are also depicted. Communication links 1412-1418 can each be a wired connection and / or a wireless connection. In the case where the two devices are located in the same device housing, the communication link may include, for example, wires, cables, and / or a communication bus located on a printed circuit board, etc. In the case where the two devices are separated from each other in different device housings, the communication link can be a wired connection and / or a wireless connection. Wired connections may include, but are not limited to, Ethernet connections, universal serial bus (USB) connections, and / or another type of physical connection. Wireless connections may include, but are not limited to, cellular connections, Wi-Fi connections, Bluetooth ®connection, a mesh network connection, and / or another type of connection using a wireless communication protocol. Some embodiments of communication links 1412-1418 may use a direct connection such as Bluetooth ® The communication links 1412-1418 may be connected and / or may use connections routed through one or more networks or network devices (not shown), such as Ethernet networks, Wi-Fi networks, cellular networks, satellite networks, intranets, extranets, the Internet, and / or Internet backbone networks, etc. Various combinations of wired connections and / or wireless connections may be used for the communication links 1412-1418.

[0110] Various aspects of the insulin delivery system 1400 are described below. Additional aspects and details may be described in the following U.S. Patent Nos.: 4,562,751; 4,685,903; 5,080,653; 5,505,709; 5,097,122; 6,485,465; 6,554,798; 6,558,320; 6,558,351; 6,641,533; 6,659,980; 6,752,787; 6,817,990; 6,932,584; and 7,621,893. The entire contents of each of the foregoing U.S. Patents are hereby incorporated herein by reference.

[0111] Delivery device 1402 is configured to deliver a therapeutic substance (e.g., insulin) to person 1401. Delivery device 1402 may be secured to person 1401 (e.g., secured to the body or clothing of person 1401) or may be implanted on or in the body of person 1401. In some embodiments, delivery device 1402 may include a reservoir, an actuator, a delivery mechanism, and a cannula (not shown). The reservoir may be configured to store a certain amount of therapeutic substance. In some embodiments, the reservoir may be refillable or replaceable. The actuator may be configured to drive the delivery mechanism. In some examples, the actuator may include a motor, such as an electric motor. The delivery mechanism may be configured to move the therapeutic substance from the reservoir through the cannula. In some examples, the delivery mechanism may include a pump and / or a plunger. The cannula may facilitate fluid connection between the reservoir and the body of person 1401. The cannula and / or needle may facilitate delivery of the therapeutic substance to a tissue layer, vein, or body cavity of person 1401. During operation, the actuator, in response to a signal (eg, a command signal), may drive the delivery mechanism, thereby moving the therapeutic substance from the reservoir, through the cannula, and into the body of person 1401 .

[0112] The components of the delivery device 1402 described above are provided as examples only. The delivery device 1402 may include other components, such as, but not limited to, a power source, a communication transceiver, computing resources, and / or a user interface, etc. One skilled in the art will recognize various implementations of the delivery device 1402 and components of such implementations. All such implementations and components are contemplated to be within the scope of the present disclosure.

[0113] Monitoring device 1404 is configured to detect a physiological condition (e.g., a glucose concentration level) of person 1401, and may also be configured to detect other things. Monitoring device 1404 may be secured to the body of person 1401 (e.g., secured to the skin of person 1401 via an adhesive) and / or may be at least partially implanted in the body of person 1401. Depending on the particular location or configuration, monitoring device 1404 may be in contact with biological matter (e.g., interstitial fluid and / or blood) of person 1401.

[0114] The monitoring device 1404 includes one or more sensors (not shown), such as, but not limited to, electrochemical sensors, electrical sensors, and / or optical sensors. As will be understood by those skilled in the art, the electrochemical sensor may be configured to respond to the interaction or binding of the biomarker with the substrate by generating an electrical signal based on the potential, conductivity, and / or impedance of the substrate. The substrate may include a material selected to interact with a specific biomarker such as glucose. The potential, conductivity, and / or impedance may be proportional to the concentration of the specific biomarker. In the case of an electrical sensor, and as will be understood by those skilled in the art, the electrical sensor may be configured to respond to the electrical biosignal by generating an electrical signal based on the amplitude, frequency, and / or phase of the electrical biosignal. The electrical biosignal may include a change in the current generated by the sum of the potential differences across tissues of the person 1401, such as the nervous system. In some embodiments, the electrical biosignal may include a portion of the potential changes generated by the heart of the person 1401 over time, for example, recorded as an electrocardiogram indicating the glucose level of the person 1401. In the case of an optical sensor, as will be appreciated by those skilled in the art, the optical sensor can be configured to respond to the interaction or binding of a biomarker with a substrate by generating an electrical signal based on changes in the brightness of the substrate. For example, the substrate can include a material selected to fluoresce in response to contact with a selected biomarker, such as glucose. The fluorescence can be proportional to the concentration of the selected biomarker.

[0115] In some embodiments, monitoring device 1404 may include other types of sensors that may be worn, carried, or coupled to person 1401 to measure activities of person 1401 that may affect glucose levels or glycemic response of person 1401. For example, a sensor may include an accelerometer configured to detect acceleration of person 1401 or a portion of person 1401, such as a hand or foot of the person. Acceleration (or lack of acceleration) may indicate exercise, sleep, or food / beverage consumption activities of person 1401, which may affect glycemic response of person 1401. In some embodiments, the sensor may include heart rate and / or body temperature, which may indicate the amount of physical exercise experienced by person 1401. In some embodiments, the sensor may include a global positioning system (GPS) receiver that detects GPS signals to determine the location of person 1401.

[0116] The above sensors are provided as examples only. Other sensors or other types of sensors for monitoring physiological conditions, activities and / or positions, etc. will be recognized by those skilled in the art and are contemplated within the scope of the present disclosure. For any sensor, the signal provided by the sensor shall be referred to as a "sensor signal".

[0117] Monitoring device 1404 may include components and / or circuits configured to pre-process sensor signals. Pre-processing may include, but is not limited to, amplification, filtering, attenuation, scaling, isolation, normalization, transformation, sampling, and / or analog-to-digital conversion, etc. Those skilled in the art will recognize various specific implementations of such pre-processing, including but not limited to specific implementations using processors, controllers, integrated circuits, application specific integrated circuits (ASICs), hardware, firmware, programmable logic devices, and / or machine executable instructions, etc. The types of pre-processing and their specific implementations are provided only as examples. It is conceivable that other types of pre-processing and specific implementations are within the scope of the present disclosure. In some embodiments, monitoring device 1404 may not perform pre-processing.

[0118] As used herein, the term "sensed data" shall mean and include information represented by a sensor signal or by a pre-processed sensor signal. In some embodiments, the sensed data may include glucose levels in the body of person 1401, acceleration of a portion of person 1401, heart rate of person 1401, temperature of person 1401, and / or geographic location (e.g., GPS location) of person 1401, etc. Monitoring device 1404 may transmit the sensed data to delivery device 1402 via communication link 1412 and / or transmit the sensed data to computing device 1406 via communication link 1414. Use of the sensed data by delivery device 1402 and / or computing device 1406 will be described later herein.

[0119] Computing device 1406 provides processing capabilities and can be implemented in a variety of ways. In some embodiments, computing device 1406 can be a consumer device such as a smartphone, a computerized wearable device (e.g., a smart watch), a tablet computer, a laptop computer, or a desktop computer, or the like, or can be a dedicated device (e.g., a portable control device) provided by, for example, a manufacturer of delivery device 1402. In some embodiments, computing device 1406 can be a "processing circuit" (defined below) integrated with another device such as delivery device 1402. In some embodiments, computing device 1406 can be secured to person 1401 (e.g., secured to the body or clothing of person 1401), can be at least partially implanted in the body of person 1401, and / or can be held by person 1401.

[0120] For each of the embodiments of computing device 1406, computing device 1406 may include various types of logic circuits, including but not limited to microprocessors, controllers, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), central processing units (CPUs), graphics processing units (GPUs), programmable logic devices, memories (e.g., random access memories, volatile memories, non-volatile memories, etc.), or other discrete or integrated logic circuits, and combinations of such components. The term "processing circuit" may generally refer to any of the foregoing logic circuits, alone or in combination with other logic circuits, or any other circuits for performing computations.

[0121] Aspects of the delivery device 1402, the monitoring device 1404, and the computing device 1406 have been described above. One or more of the devices 1402-1406 may include a user interface (not shown) that presents information to the person 1401 and / or receives information from the person 1401. The user interface may include a graphical user interface (GUI), a display device, a keyboard, a touch screen, a speaker, a microphone, a vibration motor, a button, a switch, and / or other types of user interfaces. Those skilled in the art will recognize the various types of user interfaces that can be used, and all such user interfaces are contemplated to be within the scope of the present disclosure. For example, in the case where the computing device 1406 is a consumer device such as a smart phone, a tablet computer, a laptop computer, etc., the user interface will include a display device, a physical and / or virtual keyboard, and / or an audio speaker, etc. provided by such a consumer device. In some embodiments, the user interface may notify the person 1401 of the sensed data (e.g., glucose level) and / or insulin delivery data (e.g., historical, current, or future insulin delivery rates) and may present an alert to the person 1401. In some embodiments, the user interface may receive input from person 1401, which may include, for example, requested insulin delivery changes and / or meal instructions, etc. The above description and embodiments of user interfaces are provided merely as examples, and other types and other uses of user interfaces are contemplated to be within the scope of the present disclosure.

[0122] The following describes the communication between devices 1402-1406 and the cooperation between devices 1402-1406 with respect to insulin delivery. Fig.14As depicted, and as described above, devices 1402-1406 may communicate with each other via communication links 1412-1416. In some embodiments, computing device 1406 may control the operation of delivery device 1402 and / or monitoring device 1404. For example, computing device 1406 may generate one or more signals (e.g., command signals) that cause delivery device 1402 to deliver insulin to person 1401, for example, in a basal dose and / or a bolus dose. In some embodiments, computing device 1406 may receive data associated with insulin delivery (e.g., insulin delivery data) from delivery device 1402 and / or sensed data (e.g., glucose levels) from monitoring device 1404, and may perform calculations based on the insulin delivery data, sensed data, and / or other data to control delivery device 1402. Insulin delivery data may include, but is not limited to, the type of insulin delivered, historical insulin delivery rate and / or amount, current insulin delivery rate and / or amount, and / or user input that affects insulin delivery. As will be appreciated by those skilled in the art, in a closed-loop mode of operation, computing device 1406 may transmit a dosage command to delivery device 1402 based on the difference between the current glucose level in the body of person 1401 (e.g., received from monitoring device 1404) and the target glucose level (e.g., determined by computing device 1406). The dosage command may indicate the amount of insulin to be delivered and / or the insulin delivery rate, and the current glucose level may be adjusted toward the target glucose level. Examples of closed-loop operation for insulin infusion systems are described in the following U.S. Patent Nos.: 6,088,608, 6,119,028, 6,589,229, 6,740,072, 6,827,702, 7,323,142, and 7,402,153, and in U.S. Patent Application Publication Nos. 2014 / 0066887 and 2014 / 0066889. The entire contents of each of the aforementioned patents and publications are hereby incorporated herein by reference.

[0123] The remote or cloud computing system 1408 may be a proprietary remote / cloud computing system or a commercial cloud computing system including one or more server computing devices. When the computing resources of the client computing device (e.g., computing device 1406) are insufficient, the remote / cloud computing system 1408 may provide additional computing resources on demand as needed. The computing device 1406 and the remote / cloud computing system 108 may communicate with each other via a communication link 1418, which may traverse one or more communication networks (not shown). The communication network may include, but is not limited to, an Ethernet network, a Wi-Fi network, a cellular network, a satellite network, an intranet, an extranet, the Internet, and / or an Internet backbone network. Those skilled in the art will recognize the specific implementation of the remote / cloud computing system 1408 and how to interact with such a system through various types of networks. For example, the remote / cloud computing system 1408 may include a processing circuit array (defined above) and may execute machine-readable instructions. Such specific implementations, interfaces, and networks are conceivable within the scope of the present disclosure.

[0124] In some embodiments, therapy can be administered based on transmitting a therapy determination to a therapy delivery device. Fig.14 As an example, a therapy determination (e.g., an insulin amount or an adjusted insulin amount) may be transmitted from a remote / cloud computing system 1408 to a delivery device 1402 via a computing device 1406. Alternatively, as in conjunction with Figure 1 As discussed in the exemplary system of FIG. 1 , therapy determination may be made directly or through methods such as Fig.14 The computing device 1406 in the embodiment transmits the information from the monitoring device to the therapy delivery device.

[0125] Fig.15 An exemplary insulin delivery device 1500 is depicted, which may implement a therapy delivery device (e.g., delivery device 120 or delivery device 1402) in any of the above-described systems. In an insulin delivery device such as device 1500, insulin delivery may be performed based on internal communications between a central computing module (e.g., a microcontroller for the entire device 1500) and an insulin delivery module (e.g., including the microcontroller, motor, and pump). For example, insulin delivery may be caused by the central computing module transmitting a delivery command in the form of an electrical signal that travels to the insulin delivery module via a communication structure. The central computing module may also be configured to communicate with a remote or cloud computing system (e.g., a microcontroller for the entire device 1500) to communicate with the insulin delivery module. Fig.14 1408) computing device (e.g., Fig.14 According to the techniques described herein, delivery device 1500 can transmit various event data to a remote or cloud computing system, which can transmit insulin delivery determination results to delivery device 1500.

[0126] The insulin delivery device 1500 can provide fast-acting insulin through a small tube 1510, which is configured to be connected to the fluid of a cannula inserted subcutaneously. The device 1500 can deliver two types of doses: a basic dose, which can be delivered in micro-doses periodically (e.g., every five minutes) throughout the day and night; and a bolus dose to cover the blood sugar increase caused by a meal and / or to correct high blood sugar levels. The depicted insulin delivery device 1500 includes a user interface with a button element 1520, which can be manipulated to administer an insulin bolus, change therapy settings, change user preferences, and select display features, etc. The insulin delivery device 1500 also includes a display device 1530 that can be used to present various types of information or data to the user. According to various aspects of the present disclosure, a user of the insulin delivery device 1500 can use the button element 1520 to enter certain event data (e.g., event type, event start time, event details, etc.), and can use the display device 1530 to confirm the user input. Fig.12 The insulin delivery device 1500 is provided by way of example only, and other types of insulin delivery devices and other technologies than those described above are considered to be within the scope of the present disclosure.

[0127] The embodiments disclosed herein are examples of the present disclosure and can be embodied in various forms. For example, although some embodiments herein are described as separate embodiments, each of these embodiments herein can be combined with one or more of the other embodiments herein. Various programs or components can be appropriately omitted, substituted or added to each embodiment. For example, in an alternative configuration, the described method can be performed in a different order than described, and / or various stages can be added, omitted and / or combined. The specific structural and functional details disclosed herein should not be construed as restrictive, but as the basis of the claims, and as a representative basis for teaching those skilled in the art to use the present disclosure differently with almost any suitable detailed structure. In the entire description of the drawings, similar figure numerals may refer to similar elements.

[0128] Any of the techniques, operations, methods, programs, algorithms or codes described herein may be converted into or expressed in a programming language or computer program embodied on a computer, processor or machine-readable medium. As used herein, the terms "programming language" and "computer program" each include any language used to specify instructions for a computer or processor, and include (but are not limited to) the following languages ​​and their derivatives: assembler, Basic, Batch file, BCPL, C, C+, C++, Delphi, Fortran, Java, JavaScript, machine code, operating system command language, Pascal, Perl, first generation programming language (PL / 1), Python, script processing language, visual Basic, metalanguages ​​that specify programs themselves, and all first generation, second generation, third generation, fourth generation, fifth generation or higher generation computer languages. Databases and other data models and any other metalanguages ​​are also included. No distinction is made between interpreted, compiled languages, or languages ​​that use compiled and interpreted methods at the same time. No distinction is made between compiled and source versions of the program. Thus, a reference to a program in a programming language that can exist in more than one state (such as source, compiled, object, or linked) is a reference to any and all such states. A reference to a program may encompass the actual instructions and / or the intent of those instructions.

[0129] As used herein, the terms "and" and "or" may include multiple meanings, which are also expected to depend at least in part on the context in which the terms are used. Generally, if used in an associative list (such as A, B, or C), "or" is intended to mean A, B, and C (used herein in an inclusive sense) as well as A, B, or C (used herein in an exclusive sense). In addition, the term "one or more" used herein may be used to describe any feature, structure, or characteristic of the singular, or may be used to describe some combination of feature, structure, or characteristic. However, it should be noted that this is merely an illustrative example, and the subject matter claimed is not limited to this example. In addition, if used in an associative list (such as A, B, or C), the term "at least one" may be interpreted to mean any combination of A, B, and / or C, such as A, AB, AC, BC, AA, ABC, AAB, AABBCCC, etc.

[0130] It should be understood that the foregoing description only illustrates the present disclosure. To the extent consistent, any or all aspects described in detail herein may be used in combination with any or all other aspects described in detail herein. Without departing from the present disclosure, those skilled in the art may design various alternatives and modifications. Therefore, the present disclosure is intended to cover all such alternatives, modifications and variations. The embodiments described with reference to the accompanying drawings are presented only to show certain examples of the present disclosure. Other elements, steps, methods and techniques that are not significantly different from those described above and / or in the appended claims are also intended to be included within the scope of the present disclosure.

[0131] Although several embodiments of the present disclosure have been shown in the accompanying drawings, it is not intended to limit the present disclosure thereto, as it is intended that the present disclosure be as broad as the art will allow and the specification should be read in the same manner. Therefore, the above description should not be construed as limiting, but merely as an illustration of a specific embodiment. Those skilled in the art will be able to envision other modifications within the scope and spirit of the claims appended hereto.

[0132] In view of this description, embodiments may include different combinations of features. Implementation examples are described in the following numbered clauses: Clause 1. A system comprising: one or more processors; and one or more processor-readable media storing instructions that, when executed by the one or more processors, cause the following operations to be performed: obtaining a glucose value and a glucose rate of change (ROC) based on a glucose measurement performed on a person; calculating an initial amount of insulin delivered to the person based on the glucose value; determining that the glucose value exceeds a glucose threshold and that the glucose ROC exceeds a ROC threshold; obtaining an adjustment value in response to the determination, wherein: the adjustment value represents an adjustment to be applied to the initial amount of insulin when the glucose threshold and the ROC threshold are exceeded, and the adjustment value is determined based on an earlier glucose measurement performed on the person; and adjusting the initial amount of insulin according to the adjustment value.

[0133] Clause 2. The system of clause 1, wherein when executed by the one or more processors, the instructions cause the system to adjust the initial insulin amount by at least one of: The adjustment value or the corresponding adjusted insulin amount is transmitted to an insulin delivery device; or the adjustment value or the corresponding adjusted insulin amount is presented to a user via an output device.

[0134] Clause 3. A system according to clause 1 or 2, wherein the adjustment value is a proportional factor, which is multiplied by the initial insulin amount to calculate the adjusted insulin amount.

[0135] Clause 4. The system of any one of clauses 1 to 3, wherein the glucose threshold is a value specific to the person.

[0136] Clause 5. The system of any one of clauses 1 to 4, wherein the ROC threshold is a value specific to the person.

[0137] Clause 6. A system according to any one of clauses 1 to 5, wherein the glucose threshold, the ROC threshold and the adjustment value are determined by simulating the glucose level of the person under different insulin dosage scenarios using a physiological model derived from the earlier glucose measurements.

[0138] Clause 7. A system according to clause 6, wherein: the simulation involves identifying different combinations of glucose thresholds, ROC thresholds, and adjustment values, wherein the combinations are associated with an increase in time spent within a target glucose range relative to delivery of an unadjusted insulin dose, and the glucose thresholds, the ROC thresholds, and the adjustment values ​​are based on the identified combinations.

[0139] Clause 8. A system according to any one of clauses 1 to 7, wherein the adjustment value is stored in a memory accessible to the one or more processors and is associated with the glucose threshold and the ROC threshold, and wherein obtaining the adjustment value includes retrieving the adjustment value from the memory.

[0140] Clause 9. A system according to any one of clauses 1 to 8, wherein: the glucose threshold is at least 100 mg / dL and is exceeded when the glucose value rises above the glucose threshold; and the ROC threshold is a positive value of 0.25 mg / dL or more per minute and is exceeded when the glucose ROC rises above the ROC threshold.

[0141] Clause 10. A system according to any one of clauses 1 to 8, wherein: the glucose threshold is at most 150 mg / dL and is exceeded when the glucose value drops below the glucose threshold; and the ROC threshold is a negative value of -0.25 mg / dL or less per minute and is exceeded when the glucose ROC drops below the ROC threshold.

[0142] Clause 11. A processor-implemented method, the processor-implemented method comprising: obtaining a glucose value and a glucose rate of change (ROC) based on a glucose measurement performed on a person; calculating, by one or more processors of a computing device, an initial amount of insulin delivered to the person based on the glucose value; determining, by the one or more processors, that the glucose value exceeds a glucose threshold and that the glucose ROC exceeds a ROC threshold; obtaining an adjustment value in response to the determination, wherein: the adjustment value represents an adjustment to be applied to the initial amount of insulin when the glucose threshold and the ROC threshold are exceeded, and the adjustment value is determined based on an earlier glucose measurement performed on the person; and adjusting, by the computing device, the initial amount of insulin according to the adjustment value.

[0143] Clause 12. A processor-implemented method according to clause 11, wherein adjusting the initial insulin amount comprises at least one of the following operations: transmitting the adjustment value or the corresponding adjusted insulin amount to an insulin delivery device; or presenting the adjustment value or the corresponding adjusted insulin amount to a user through an output device.

[0144] Clause 13. The processor-implemented method of clause 11 or 12, wherein the adjustment value is a scaling factor that is multiplied by the initial insulin amount to calculate the adjusted insulin amount.

[0145] Clause 14. A processor-implemented method according to any one of clauses 11 to 13, wherein the glucose threshold, the ROC threshold, and the adjustment value are determined by simulating the glucose level of the person under different insulin dosage scenarios using a physiological model derived from the earlier glucose measurements.

[0146] Clause 15. A processor-implemented method according to clause 14, wherein: the simulation involves identifying different combinations of glucose thresholds, ROC thresholds, and adjustment values ​​that are associated with an increase in time spent within a target glucose range relative to delivering an unadjusted insulin dose, and the glucose thresholds, the ROC thresholds, and the adjustment values ​​are based on the identified combinations.

[0147] Clause 16. A processor-implemented method according to any one of clauses 11 to 15, wherein the adjustment value is stored in a memory accessible to the one or more processors and is associated with the glucose threshold and the ROC threshold, and wherein obtaining the adjustment value comprises retrieving the adjustment value from the memory.

[0148] Clause 17. A processor-implemented method according to any one of clauses 11 to 16, wherein: the glucose threshold is at least 100 mg / dL and is exceeded when the glucose value rises above the glucose threshold; and the ROC threshold is a positive value of 0.25 mg / dL or more per minute and is exceeded when the glucose ROC rises above the ROC threshold.

[0149] Clause 18. A processor-implemented method according to any one of clauses 11 to 16, wherein: the glucose threshold is at most 150 mg / dL and is exceeded when the glucose value drops below the glucose threshold; and the ROC threshold is a negative value of -0.25 mg / dL or less per minute and is exceeded when the glucose ROC drops below the ROC threshold.

[0150] Item 19. One or more processor-readable media storing instructions that, when executed by one or more processors, result in the following operations: obtaining a glucose value and a glucose rate of change (ROC) value based on a glucose measurement performed on a person; calculating an initial amount of insulin delivered to the person based on the glucose value; determining that the glucose value exceeds a glucose threshold and that the glucose ROC exceeds a ROC threshold; obtaining an adjustment value in response to the determination, wherein: the adjustment value represents an adjustment to be applied to the initial amount of insulin when the glucose threshold and the ROC threshold are exceeded, and the adjustment value is determined based on an earlier glucose measurement performed on the person; and adjusting the initial amount of insulin according to the adjustment value.

[0151] Clause 20. A processor-readable medium as described in clause 19, wherein: the glucose threshold, the ROC threshold, and the adjustment value are determined by simulating the glucose level of the person under different insulin dosage scenarios using a physiological model derived from the earlier glucose measurements, and the simulation involves identifying different combinations of glucose thresholds, ROC thresholds, and adjustment values ​​that are associated with an increase in time spent within a target glucose range relative to delivering an unadjusted insulin dose.

Claims

1. A system, comprising: one or more processors; as well as One or more processor-readable media storing instructions that, when executed by the one or more processors, cause the following operations to be performed: Based on the glucose measurements performed on the person, glucose values ​​and glucose rate of change (ROC) are obtained; calculating an initial amount of insulin to be delivered to the human based on the glucose value; determining that the glucose value exceeds a glucose threshold and that the glucose ROC exceeds a ROC threshold; An adjustment value is obtained in response to the determining, wherein: The adjustment value represents an adjustment to be applied to the initial insulin amount when the glucose threshold and the ROC threshold are exceeded, and The adjustment value is determined based on an earlier glucose measurement performed on the person; and The initial insulin amount is adjusted according to the adjustment value.

2. The system of claim 1, wherein when executed by the one or more processors, the instructions cause the system to adjust the initial insulin amount by at least one of: transmitting the adjustment value or the corresponding adjusted insulin amount to an insulin delivery device; or The adjustment value or the corresponding adjusted insulin amount is presented to the user via an output device.

3. The system of claim 1, wherein the adjustment value is a scaling factor that is multiplied by the initial insulin amount to calculate the adjusted insulin amount.

4. The system of claim 1, wherein the glucose threshold is a value specific to the person. The system of claim 1 , wherein the ROC threshold is a value specific to the person.

6. The system of claim 1, wherein the glucose threshold, the ROC threshold, and the adjustment value are determined by simulating glucose levels of the person under different insulin dosing scenarios using a physiological model derived from the earlier glucose measurements.

7. The system of claim 6, wherein: The simulation involves identifying different combinations of glucose thresholds, ROC thresholds, and adjustment values ​​that are associated with an increase in time spent within a target glucose range relative to delivery of an unadjusted insulin dose, and The glucose threshold, the ROC threshold, and the adjustment value are based on the identified combination.

8. The system of claim 1, wherein the adjustment value is stored in a memory accessible to the one or more processors and is associated with the glucose threshold and the ROC threshold, and wherein obtaining the adjustment value comprises retrieving the adjustment value from the memory.

9. The system of claim 1, wherein: The glucose threshold is at least 100 mg / dL and is exceeded when the glucose value rises above the glucose threshold; and The ROC threshold is a positive value of 0.25 mg / dL per minute or greater and is exceeded when the glucose ROC rises above the ROC threshold.

10. The system of claim 1, wherein: The glucose threshold is at most 150 mg / dL and is exceeded when the glucose value falls below the glucose threshold; and The ROC threshold is a negative value of -0.25 mg / dL or less per minute and is exceeded when the glucose ROC falls below the ROC threshold.

11. A processor-implemented method, the processor-implemented method comprising: Based on the glucose measurements performed on the person, glucose values ​​and glucose rate of change (ROC) are obtained; calculating, by one or more processors of a computing device, an initial amount of insulin to be delivered to the human based on the glucose value; determining, by the one or more processors, that the glucose value exceeds a glucose threshold and that the glucose ROC exceeds a ROC threshold; An adjustment value is obtained in response to the determining, wherein: The adjustment value represents an adjustment to be applied to the initial insulin amount when the glucose threshold and the ROC threshold are exceeded, and The adjustment value is determined based on an earlier glucose measurement performed on the person; and The initial insulin amount is adjusted by the calculation device according to the adjustment value.

12. The processor-implemented method of claim 11, wherein adjusting the initial insulin amount comprises at least one of: transmitting the adjustment value or the corresponding adjusted insulin amount to an insulin delivery device; or The adjustment value or the corresponding adjusted insulin amount is presented to the user via an output device.

13. The processor-implemented method of claim 11, wherein the adjustment value is a scaling factor that is multiplied by the initial insulin amount to calculate the adjusted insulin amount.

14. The processor-implemented method of claim 11, wherein the glucose threshold, the ROC threshold, and the adjustment value are determined by simulating glucose levels of the person under different insulin dosing scenarios using a physiological model derived from the earlier glucose measurements.

15. The processor-implemented method of claim 14, wherein: The simulation involves identifying different combinations of glucose thresholds, ROC thresholds, and adjustment values ​​that are associated with an increase in time spent within a target glucose range relative to delivery of an unadjusted insulin dose, and The glucose threshold, the ROC threshold, and the adjustment value are based on the identified combination.

16. The processor-implemented method of claim 11, wherein the adjustment value is stored in a memory accessible to the one or more processors and is associated with the glucose threshold and the ROC threshold, and wherein obtaining the adjustment value comprises retrieving the adjustment value from the memory.

17. The processor-implemented method of claim 11, wherein: The glucose threshold is at least 100 mg / dL and is exceeded when the glucose value rises above the glucose threshold; and The ROC threshold is a positive value of 0.25 mg / dL per minute or greater and is exceeded when the glucose ROC rises above the ROC threshold.

18. The processor-implemented method of claim 11, wherein: The glucose threshold is at most 150 mg / dL and is exceeded when the glucose value falls below the glucose threshold; and The ROC threshold is a negative value of -0.25 mg / dL or less per minute and is exceeded when the glucose ROC falls below the ROC threshold.

19. One or more processor-readable media storing instructions that, when executed by one or more processors, result in the following operations: Based on the glucose measurement performed on the person, a glucose value and a glucose rate of change (ROC) value are obtained; calculating an initial amount of insulin to be delivered to the human based on the glucose value; determining that the glucose value exceeds a glucose threshold and that the glucose ROC exceeds a ROC threshold; An adjustment value is obtained in response to the determining, wherein: The adjustment value represents an adjustment to be applied to the initial insulin amount when the glucose threshold and the ROC threshold are exceeded, and The adjustment value is determined based on an earlier glucose measurement performed on the person; and The initial insulin amount is adjusted according to the adjustment value.

20. The processor readable medium of claim 19, wherein: The glucose threshold, the ROC threshold, and the adjustment value are determined by simulating glucose levels of the person under different insulin dosing scenarios using a physiological model derived from the earlier glucose measurements, and The simulations involved identifying different combinations of glucose thresholds, ROC thresholds, and adjustment values ​​that were associated with increases in time spent within the target glucose range relative to delivery of an unadjusted insulin dose.

Images