Determination of carbohydrate to insulin ratio

Abstract

Techniques related to determining medical parameters are disclosed herein. In some embodiments, the technique comprises: obtaining a measure of an insulin dose of a patient; and determining at least one carbohydrate to insulin ratio of the patient based at least in part on a model and using the obtained measure of insulin dose of the patient, wherein parameters of the model are determined based on data associated with a population of patients.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 63 / 599,815, filed on November 16, 2023, entitled “DETERMINATION OF CARBOHYDRATE-TO-INSULIN RATIOS,” the entire contents of which are incorporated herein by reference for all purposes. Technical Field

[0003] The present disclosure generally relates to the determination of carbohydrate to insulin ratios. Background Art

[0004] The carbohydrate to insulin ratio is a key parameter for drug (e.g., insulin) delivery. The carbohydrate to insulin ratio can vary significantly from person to person, and accurately setting the carbohydrate to insulin ratio parameter is critical for safety and drug effectiveness, such as for insulin pumps. However, it can be difficult to determine an accurate and safe value, or an accurate and safe range of values. Summary of the invention

[0005] Disclosed herein are techniques related to the determination of carbohydrate to insulin ratios. 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 processor-readable media (e.g., non-transitory processor-readable media).

[0006] In one aspect, a processor-implemented method is disclosed. In some embodiments, the processor-implemented method includes: obtaining a measure of a patient's insulin dosage; and determining at least one carbohydrate-to-insulin ratio of the patient based at least in part on a model and using the obtained measure of the patient's insulin dosage, wherein parameters of the model are determined based on data associated with a patient population.

[0007] In another aspect, a system is disclosed. The system may include one or more processors and one or more processor-readable media storing instructions that, when executed by the one or more processors, cause one or more operations to be performed, the operations including: obtaining a measure of an insulin dose for a patient; and determining at least one carbohydrate-to-insulin ratio for the patient based at least in part on a model and using the obtained measure of the insulin dose for the patient, wherein parameters of the model are determined based on data associated with a patient population.

[0008] In another aspect, one or more processor-readable media are disclosed that store instructions. In some embodiments, the instructions, when executed by one or more processors, cause the following operations to be performed: obtaining a measure of an insulin dose for a patient; and determining at least one carbohydrate to insulin ratio for the patient based at least in part on a model and using the obtained measure of the insulin dose for the patient, wherein parameters of the model are determined based on data associated with a patient population.

[0009] In another aspect, a processor-implemented method is disclosed. In some embodiments, the processor-implemented method includes: obtaining a total daily dose (TDD) of insulin for a patient; and determining at least one carbohydrate-to-insulin ratio for the patient, wherein for a TDD of less than 10 units, the carbohydrate-to-insulin ratio is less than 25 grams / unit, and wherein for a TDD of less than 105 units, the carbohydrate-to-insulin ratio is greater than 5 grams / unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] 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.

[0011] Figure 1 is an illustration of an example of a therapy delivery system according to aspects of the present disclosure.

[0012] Figure 2A , Figure 2B , Figure 2C and Figure 2D A graph showing an exemplary function relating carbohydrate to insulin ratio to total daily dose (TDD) of insulin according to some embodiments.

[0013] Figure 3 is a flow chart of an exemplary process for determining at least one carbohydrate to insulin ratio for a patient based on a model, according to some embodiments.

[0014] Figure 4 is a flow chart of an exemplary process for determining at least one carbohydrate to insulin ratio for a patient based on the patient's TDD, according to some embodiments.

[0015] Figure 5 is a diagram of an example of an insulin delivery device according to aspects of the present disclosure.

[0016] Figure 6 is a block diagram of an example of a computer system that may be utilized in embodiments as described herein. DETAILED DESCRIPTION

[0017] Therapeutic substances (e.g., insulin) can be delivered to diabetic patients to manage, for example, type I diabetes or type II diabetes. The delivery of the appropriate amount of therapeutic substances at the appropriate time can help maintain blood sugar levels in the body within a target range (e.g., a normal blood sugar range) to prevent hyperglycemia or hypoglycemia conditions.

[0018] Disclosed herein are techniques for determining at least one carbohydrate to insulin ratio (CIR) for a patient. The CIR can be used to determine the insulin dose to be delivered to offset a given amount of carbohydrate consumed. For example, given X CIR in grams / unit and Y The amount of carbohydrate consumed in grams, the insulin dose is determined as Y / X The determined insulin dose may then be delivered, for example, by actuating an infusion pump so that insulin is provided to the patient.

[0019] In some embodiments, at least one CIR can be determined based on a model, wherein the parameters of the model are determined based on data associated with a patient population. For example, the data associated with a patient population can include one or more CIRs for a given patient and one or more insulin dosage metrics for the patient, wherein the model relates the one or more CIRs to the one or more insulin dosage metrics. The insulin dosage metrics may include a total daily dose (TDD) of insulin, a total bolus dose of insulin per day, total insulin within a predetermined time range or time period (e.g., morning, evening, etc.), etc.

[0020] By using a patient population to generate a model (e.g., determining the parameters that define the model), the resulting CIR can be more accurate than the conventionally determined CIR. Conventional techniques such as the 500 rule, the 350 rule, or the 300 rule estimate the CIR based on the patient's TDD, and these conventional techniques may result in overly conservative or overly aggressive CIR estimates, which may result in health and safety issues. For example, the 500 rule, the 350 rule, and / or the 300 rule may overestimate the CIR for patients with low TDD (e.g., by determining a CIR that is too conservative and / or not aggressive enough). This may particularly affect pediatric patients because TDD tends to be age-related. On the contrary, the 500 rule, the 350 rule, and / or the 300 rule may underestimate the CIR for patients with high TDD (e.g., determining a CIR that is too aggressive). This may tend to cause hypoglycemia by calculating an insulin dose that is too high for a given amount of carbohydrates. However, by using the technology disclosed herein, a more accurate CIR may be determined, such as neither too conservative nor too aggressive. It should be understood that although conventional techniques are generally described herein as the 500 rule, the 350 rule and / or the 300 rule, the technology disclosed herein differs from these conventional techniques in that a model based on population-level data is utilized to predict CIR based on TDD rather than using a fixed ratio of CIR to TDD.

[0021] In some embodiments, a model relating CIR to an insulin dosage metric can be used to determine the median CIR for a given insulin dosage. For example, for a patient with a given TDD, the model can be used to determine the median CIR for the patient with TDD. Additionally or alternatively, in some embodiments, the model can provide an indication of CIR at any other suitable percentile. For example, where the insulin dosage metric is TDD, the model can be used to determine the 10th percentile CIR used by patients with TDD, the 90th percentile CIR used by patients with TDD, and / or any other percentile. As used herein, the 90th percentile CIR is used to determine the median CIR for a patient with TDD. N Percentile CIR means N % of patients use the CIR or lower (or, alternatively, 100- N% of patients use a higher CIR). Using this model to determine the various percentiles of a given insulin dosage metric can allow a patient and / or health care provider to determine where the patient's CIR falls relative to a patient population with the same or similar insulin dosage metric. In addition, in the case where a patient or health care provider inputs a CIR (e.g., used as the setting of an infusion pump) that is higher or lower than a given percentile of the corresponding insulin dosage metric, various percentiles can be used to provide an alarm or warning. For example, in the case where the insulin dosage metric used is TDD and the patient or health care provider provides an indication of a CIR that is determined to be outside a predetermined percentile range (e.g., outside the scope of the CIR that falls within the 10th-90th percentile, outside the scope of the CIR that falls within the 25th-75th percentile, etc.), the technology disclosed herein can make it possible to provide a warning or alarm. This can allow the wrong input of CIR to be detected before use, thereby preventing health and safety issues.

[0022] The present disclosure is mainly described with respect to insulin delivery systems. Aspects of the present disclosure and each embodiment can be put into practice with one or more types of insulin (e.g., quick-acting insulin, intermediate-acting insulin and / or slow-acting insulin). For example, quick-acting insulin can be used for both basal dose and bolus dose.

[0023] Although the present disclosure is mainly described with respect to 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 for other therapy systems. For example, some technologies of the present disclosure can be applicable to the practice of glucagon delivery systems.

[0024] Discussion utilizing terms such as, for example, “process,” “compute,” “calculate,” “determine,” “establish,” “analyze,” “examine,” and the like may refer to the operations and / or processes of a computer, computing platform, computing system, or other electronic computing device that manipulates data represented as physical (e.g., electronic) quantities within a computer’s registers and / or memory and / or transforms such data into other data similarly represented as physical quantities within a computer’s registers and / or memory or other non-transitory information storage medium that may store instructions to perform the operations and / or processes by, for example, one or more processors or processor devices (e.g., a system on a chip) or devices associated with such processors.

[0025] In the context of the present invention, a "module" may refer to a set of computer executable instructions and / or a hardware processor configured to execute a set of computer executable instructions. A hardware processor may be an integrated circuit device associated with a computing device, such as a server or user device (e.g., a desktop computer, a laptop computer, a tablet computer, a mobile phone, etc.), which may be programmed to perform specific tasks. In some embodiments, multiple modules may be implemented as a single module. In some embodiments, a single module may be implemented as multiple modules. In some embodiments, two or more modules may be executed by the same device (e.g., the same computing device or delivery device).

[0026] 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 may occur or be performed simultaneously or in parallel.

[0027] Figure 1 An exemplary therapy delivery system 100 for a person 101 is depicted. The components of the therapy delivery system 100 can be used to implement one or more blocks of process 300 and / or process 400. System 100 can be an insulin delivery system. The depicted therapy delivery system 100 includes a delivery device 102, a monitoring device 104, a computing device 106, and an optional remote or cloud computing system 108. The delivery device 102, the monitoring device 104, and the computing device 106 can be embodied in various ways, including being disposed in one or more device housings. For example, in some embodiments, all devices 102 to 106 can be disposed in a single device housing. In some embodiments, each device in devices 102 to 106 can be disposed in a separate device housing. In some embodiments, two or more devices in devices 102 to 106 can be disposed in the same device housing, and / or a single device 102, 104, or 106 can have two or more parts disposed in two or more housings. Such embodiments and combinations thereof are contemplated to be within the scope of the present disclosure.

[0028] Figure 1 Communication links 112 to 118 are also depicted. Communication links 112 to 118 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. The wired connection may include, but is not limited to, an Ethernet connection, a USB connection, and / or another type of physical connection. The wireless connection may include, but is not limited to, a cellular connection, a Wi-Fi connection, a Bluetooth connection, etc. ®A connection, a mesh network connection, and / or another type of connection using a wireless communication protocol. Some embodiments of communication links 112 to 118 may use a direct connection (such as Bluetooth ® Connections) 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. Various combinations of wired connections and / or wireless connections may be used for communication links 112 to 118.

[0029] Various aspects of the insulin delivery system 100 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.

[0030] Delivery device 102 is configured to deliver a therapeutic substance (e.g., insulin) to person 101. Delivery device 102 may be fixed to person 101 (e.g., fixed to the body or clothing of person 101) or may be implanted on or in the body of person 101. In some embodiments, delivery device 102 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 101. The cannula and / or needle may facilitate delivery of the therapeutic substance to a tissue layer, vein, or body cavity of person 101. 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 101 .

[0031] The above-described components of the delivery device 102 are provided as examples only. The delivery device 102 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 102 and components of such implementations. All such implementations and components are contemplated to be within the scope of the present disclosure.

[0032] Continue to refer Figure 1 Monitoring device 104 is configured to detect a physiological condition (e.g., a glucose concentration level) of person 101, and may also be configured to detect other matters. Monitoring device 104 may be secured to the body of person 101 (e.g., secured to the skin of person 101 via an adhesive) and / or may be at least partially implanted in the body of person 101. Depending on the particular location or configuration, monitoring device 104 may come into contact with biological matter (e.g., interstitial fluid and / or blood) of person 101.

[0033] The monitoring device 104 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 changes in the current generated by the sum of the potential differences across the tissues of the person 101, 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 101 over time, for example, recorded as an electrocardiogram indicating the glucose level of the person 101. 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.

[0034] In some embodiments, monitoring device 104 may include other types of sensors that may be worn, carried, or coupled to person 101 to measure activities of person 101 that may affect glucose levels or glycemic response of person 101. For example, a sensor may include an accelerometer configured to detect acceleration of person 101 or a portion of person 101 (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 101, which may affect glycemic response of person 101. In some embodiments, the sensor may include heart rate and / or body temperature, which may indicate the amount of physical exercise experienced by person 101. In some embodiments, the sensor may include a GPS receiver that detects GPS signals to determine the location of person 101.

[0035] 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".

[0036] Monitoring device 104 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, ASICs, integrated circuits, 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 contemplated that other types of pre-processing and specific implementations are within the scope of the present disclosure. In some embodiments, monitoring device 104 may not perform pre-processing.

[0037] 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 101, acceleration of a portion of person 101, heart rate of person 101, temperature of person 101, and / or geographic location (e.g., GPS location) of person 101, etc. Monitoring device 104 may transmit the sensed data to delivery device 102 via communication link 112 and / or transmit the sensed data to computing device 106 via communication link 114. Use of the sensed data by delivery device 102 and / or computing device 106 will be described later herein.

[0038] Computing device 106 provides processing capabilities and may be implemented in a variety of ways. In some embodiments, computing device 106 may 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 may be a dedicated device (e.g., a portable control device) provided by, for example, a manufacturer of delivery device 102. In some embodiments, computing device 106 may be a "processing circuit" (defined below) integrated with another device, such as delivery device 102. In some embodiments, computing device 106 may be secured to person 101 (e.g., secured to the body or clothing of person 101), may be at least partially implanted in the body of person 101, and / or may be held by person 101. In some embodiments, computing device 106 may be configured to perform one or more blocks of process 300 and / or process 400, as follows: Figure 3 and Figure 4 For example, in some embodiments, computing device 106 may determine the patient's CIR based on insulin dosage metrics.

[0039] For each of the embodiments of the computing device 106, the computing device 106 may include various types of logic circuits, including but not limited to microprocessors, microcontrollers, 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, as well as 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.

[0040] Aspects of delivery device 102, monitoring device 104, and computing device 106 have been described above. One or more of devices 102 to 106 may include a user interface (not shown) that presents information to person 101 and / or receives information from person 101. 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 computing device 106 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 person 101 of 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 person 101. In some embodiments, the user interface may receive input from person 101, which may include, for example, a requested insulin delivery change and / or a meal instruction, 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 as being within the scope of the present disclosure.

[0041] The following describes the communication between devices 102 to 106 and the cooperation between devices 102 to 106 with respect to insulin delivery. Figure 1As depicted, and as described above, devices 102-106 may communicate with each other via communication links 112-116. In some embodiments, computing device 106 may control the operation of delivery device 102 and / or monitoring device 104. For example, computing device 106 may generate one or more signals (e.g., command signals) that cause delivery device 102 to deliver insulin to person 101, e.g., as a basal dose and / or a bolus dose. In some embodiments, computing device 106 may receive data associated with insulin delivery (e.g., insulin delivery data) from delivery device 102 and / or sensed data (e.g., glucose levels) from monitoring device 104, and may perform calculations based on the insulin delivery data, sensed data, and / or other data to control delivery device 102. 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 106 may transmit a dosage command to delivery device 102 based on the difference between the current glucose level in the body of person 101 (e.g., received from monitoring device 104) and the target glucose level (e.g., determined by computing device 106). 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.

[0042] Continue to refer Figure 1, the remote or cloud computing system 108 can 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 106) are insufficient, the remote / cloud computing system 108 can provide additional computing resources on demand as needed. The computing device 106 and the remote / cloud computing system 108 can communicate with each other via a communication link 118, which can 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 108 and how to interact with such a system through various types of networks. For example, the remote / cloud computing system 108 may include a processing circuit array (defined above) and may execute machine-readable instructions. It is contemplated that such specific implementations, interfaces, and networks are within the scope of the present disclosure. In some embodiments, the remote or cloud computing system 108 may be configured to perform one or more blocks of process 300 and / or process 400, as respectively described in Figure 3 and Figure 4 . For example, in some embodiments, the remote or cloud computing system 108 can be configured to determine the CIR of the patient. In some embodiments, the remote or cloud computing system 108 can store and / or analyze data from a patient population, which is used to determine the model parameters of a model that relates the insulin dosage measurement to the CIR. In some embodiments, the remote or cloud computing system 108 can determine the model parameters of the model. In some embodiments, the remote or cloud computing system 108 can store a lookup table that includes results from the model, for example, indicating the CIR of different values ​​for the insulin dosage measurement. In some such embodiments, the remote or cloud computing system 108 can transmit any suitable information, such as data from a lookup table, model parameters, and / or the determined CIR, to a computing system (e.g., computing system 106) associated with a specific medical device for use with the medical device.

[0043] Exemplary therapy delivery systems have been described above. For convenience, the following description may be primarily with reference to an insulin delivery system as an example of a therapy delivery system. However, any aspect, embodiment, or description intended to be relevant to an insulin delivery system should be applicable to a therapy delivery system that delivers a therapy other than insulin.

[0044] Figure 2AAn exemplary graph illustrating the CIR as a function of TDD (in units per day) is depicted. As illustrated, curve 202 is a model fit to population data, wherein the model fit is determined based on the CIR of a group of patients based on the TDD of each patient. Curve 204 depicts the 90th percentile CIR of each TDD, and curve 206 depicts the 10th percentile CIR of each TDD. Note that curve 202, curve 204, and curve 206 each utilize one or more mathematical functions to approximate exponential decay. Mathematical functions may include any combination of exponential functions, polynomial functions, etc. In some embodiments, curve 202 may be generated by fitting one or more mathematical functions to the median data of each TDD, curve 204 may be generated by fitting one or more mathematical functions to the 90th percentile data of each TDD, and curve 206 may be generated by fitting one or more mathematical functions to the 10th percentile data of each TDD.

[0045] Conventional technology can utilize the rule commonly referred to as 500 rule, 350 rule or 300 rule to determine CIR based on the TDD of a given patient. For example, using the 500 rule, the CIR of the patient can be determined as 500 divided by the TDD. Similarly, using the 350 rule, the CIR of the patient can be determined as 350 divided by the TDD. This may lead to safety and accuracy issues. For example, the CIR determined for a relatively low TDD (such as less than about 30 units per day) can be relatively high. As a more specific example, using the 500 rule, a CIR of 20 can be calculated for a TDD of 25 units per day, and using the 350 rule, a CIR of 14 can be calculated for a TDD of 25 units per day. These CIR estimates may be overly conservative (e.g., CIR may be "too high" than the CIR required for the patient to accurately calculate the insulin dose), which may lead to elevated blood glucose levels and other undesirable physiological results. Conversely, the 500 Rule and the 350 Rule may result in a relatively low CIR for TDDs above a certain threshold, such as above 35 units per day, above 50 units per day, above 70 units per day, 80 units per day, above 85 units per day, about 90 units per day, above 95 units per day, etc. For example, the 500 Rule and the 350 Rule may result in a CIR that stabilizes at about 5 for TDDs in the range of about 85 to 95 units per day, which may be overly aggressive, thereby resulting in hypoglycemia. Figure 2B Shown is a comparison of the CIR determined using the 500 rule and the 350 rule with the CIR determined using the population-based approach as described herein. Figure 2CA comparison of the CIR determined using the 500 Rule and the 350 Rule with the CIR determined using a population-based approach for TDDs less than 35 units per day is shown. Note that conventional techniques (e.g., the 500 Rule and the 350 Rule) tend to overestimate the CIR for TDDs less than about 35 units per day relative to the population-based techniques described herein. Figure 2D A comparison of the CIRs determined using the 500 rule and the 350 rule with the CIR determined using the population-based approach for TDDs greater than 35 units per day is shown. Note that for TDDs greater than 35 units per day, the CIR generated by the 350 rule is less than the CIR predicted using the population-based technique described herein.

[0046] The technology disclosed herein can determine the CIR of a given patient more accurately by utilizing the population level insulin dosage using, for example, a model fitting to the population level data to determine the CIR. Compared with the CIR that can be determined using conventional 500 rules, 350 rules and / or 300 rules, the CIR determined using the technology disclosed herein can be particularly obvious for a given patient at relatively low and relatively high TDD. For example, for a TDD of less than about 10 units, the technology disclosed herein can result in a CIR of less than about 25 grams / unit. As another example, for a TDD in the range of about 85 units to 95 units, the technology disclosed herein can result in a CIR greater than about 5 grams / unit.

[0047] In some embodiments, at least one CIR of a patient can be determined based on a model at least in part. The parameters of the model can be determined based on data associated with a patient population (e.g., one thousand patients, ten thousand patients, one hundred thousand patients, etc.). In some embodiments, the patient population can be limited to those patients with similar demographics to the patient determining the CIR. For example, similar demographics can refer to a patient population with similar age (e.g., within ten years, within five years, etc.). As a more specific example, in the case where the patient is a pediatric patient, the model can be developed using data from a pediatric patient population. In some specific implementations, pediatric patients can be in addition within the specific age range of the patient, e.g., within two years, within four years, within five years, etc. In some embodiments, multiple CIRs of a patient can be determined based on a model. For example, multiple CIRs can each correspond to different times of the day. It should be noted that, as used herein, two values ​​may be considered "similar" if they are within a predetermined range of each other, where the range may be expressed as a percentage (e.g., the difference between the first value and the second value is less than 5%, the difference is less than 10%, etc.), an absolute number (e.g., the absolute value of the difference between the two values ​​is less than a predetermined threshold), or any other suitable metric. Additionally, for non-numeric criteria, two values ​​may be considered "similar" if they are substantially the same.

[0048] In some embodiments, the data associated with the patient population used for the development model can include at least one CIR and at least one insulin dosage metric used by the patient for each patient. The insulin dosage metric can include TDD, total dose from bolus insulin only, total insulin dose in a predetermined time period (e.g., morning insulin dose, overnight insulin dose, etc.) or any combination thereof. Note that in some embodiments, patients can utilize different CIRs for different time periods, such as utilizing a first CIR for a morning time period, utilizing a second CIR for an afternoon time period, etc. In this case, the population data can include multiple CIRs for a given patient represented in the population data.

[0049] In some embodiments, the model may include one or more mathematical functions that fit data associated with the patient population. For example, the model may include one or more mathematical functions that represent a CIR that decreases at a rate proportional to its current value. As a more specific example, in some embodiments, the one or more mathematical functions may include or may approximate an exponential decay. For example, an exemplary mathematical function that can be used to determine the CIR for a patient with a given TDD may be represented as: In this equation, A , k1 , k2 , k3 and C Represents coefficients that can be fitted based on data associated with a patient population.

[0050] It should be understood that the model can be related to CIR by any suitable insulin dosage metric. In addition, in some embodiments, the model can be a model other than a model including one or more mathematical functions. For example, the model can be a machine learning model trained using a training set including data associated with a patient population.

[0051] Figure 3 3 is a flow chart of an exemplary process 300 for determining at least one CIR based at least in part on a model according to some embodiments. The blocks of process 300 may be executed by one or more processors or controllers (such as a controller of a delivery device, a computing device or system associated with a delivery device, a remote or cloud computing device, etc.). Figure 1 An example of such an apparatus is shown and described. In some embodiments, the blocks of process 300 may be executed in a manner different from that of Figure 3 In some embodiments, two or more blocks of process 300 may be performed substantially in parallel. In some embodiments, one or more blocks of process 300 may be omitted.

[0052] Process 300 can start at 302 places by obtaining the measurement of the insulin dosage associated with the patient. The example of the insulin dosage measurement that can be obtained includes TDD, total bolus dose every day, the total insulin delivered in a specific time period or delivered in a specific time period (for example, during a specific hour of a day) on average, etc. Note that in some embodiments, multiple insulin dosage measurements of the patient can be obtained, such as TDD and total bolus dose every day, TDD and total insulin delivered in a specific time period or a day, total bolus dose and total insulin delivered in a specific time period or a day, etc. The insulin dosage measurement can be obtained via user input. The example of user input can include, for example, when a new medical device is initialized (for example, when a new infusion pump is initialized), the patient inputs or the input provided by a health care professional.

[0053] At 304, process 300 can determine at least one CIR for the patient based at least in part on the model. Parameters of the model can be determined based on data associated with the patient population. As described above, in some embodiments, the patient population can include only patients who are similar to the patient associated with the obtained insulin dosage metric in terms of at least one demographic characteristic. As described above, in some embodiments, the model can include one or more mathematical functions that relate at least one insulin dosage metric to a corresponding CIR. For example, as described above in conjunction with Figure 2A and Figure 2B As shown and described, the model may include one or more mathematical functions that relate CIR to TDD. The one or more mathematical functions may include one or more mathematical functions that represent a carb ratio that decreases at a rate proportional to its current value. For example, the one or more mathematical functions may include one or more mathematical functions that contain and / or approximate exponential decay. In some embodiments, the parameters of the model may include regression coefficients of one or more mathematical functions determined at least in part based on data associated with a patient population.

[0054] It should be noted that in some embodiments, the model may include multiple sets of mathematical functions, each set associated with a different percentile of CIR for a patient population. For example, the model may include a first set of mathematical functions that express the median CIR of a patient population as an insulin dose metric (e.g., an insulin dose metric for TDD, as described above in conjunction with Figure 2A and Figure 2B As another example, the model may include a second and third set of mathematical functions that represent other percentiles of the CIR for a patient population as a function of the insulin dose metric (e.g., the 10th and 90th percentiles, as described above in conjunction with Figure 2A and Figure 2BIn some implementations, the model fit can be determined for any suitable number of percentiles (e.g., for three percentiles such as the 10th, 50th, and 90th percentiles, nine percentiles such as the 10th to 90th percentiles in ten unit increments, etc.).

[0055] In some embodiments, the output of the model (e.g., the output of one or more mathematical functions) can be stored in a table (e.g., a lookup table). The lookup table can use the insulin dosage metric as a key, wherein the corresponding value of the CIR is represented as a plurality of potential values. For example, given a TDD of, for example, 35 units / day, the lookup table can indicate that the 10th percentile CIR is 7.5 grams / unit, the median CIR is 10 grams / unit, and the 90th percentile CIR is 15 grams / unit. In some embodiments, such a lookup table can be used to identify at least one CIR.

[0056] In some embodiments, after determining at least one CIR, process 300 can, for example, present an indication of at least one CIR to a patient or health care provider to confirm that at least one CIR will be used to determine insulin administration. In some embodiments, process 300 can cause at least one CIR to be used as a medical device parameter for determining insulin administration. For example, at least one CIR can be used to initialize the setting of a medical device and / or modify the setting of a medical device.

[0057] Note that in some embodiments, the at least one CIR obtained can be determined based at least in part on the operating mode of the medical device (e.g., an infusion pump). For example, in some embodiments, in response to determining that the operating mode is a fully closed-loop operating mode (e.g., in which the patient does not need to input meal-related information, such as the number of carbohydrates in the meal or the number of meals consumed), at least one CIR can be associated with a relatively low percentile of the model fit (e.g., the 10th percentile, the 20th percentile, less than the 50th percentile, etc.). The selection of a CIR associated with a relatively low percentile can allow for more aggressive insulin administration, which can be safer in a fully closed-loop operating mode than a device operating in a manual or hybrid operating mode.

[0058] At 330, process 300 may optionally provide an indication of a range of CIRs associated with a population for insulin dosage metrics associated with a patient. For example, in some embodiments, process 300 may indicate CIRs associated with particular upper and lower percentiles, such as the 10th and 90th percentiles, the 25th and 75th percentiles, etc. In this case, providing a range of CIRs within particular percentile boundaries may allow a patient and / or health care provider to understand how conservative or aggressive a particular CIR is.

[0059] Note that an indication of at least one CIR and / or a range of CIRs associated with a population may be provided via a user interface (eg, a user interface associated with a medical device, a monitoring device, a paired mobile device, etc.).

[0060] Optionally, at 340, process 300 can cause insulin to be delivered to the patient based at least in part on the CIR determined at 320. For example, in some embodiments, process 300 can determine an insulin dose (e.g., a bolus insulin dose) based on the CIR, e.g., in response to an indication of the number of carbohydrates in a meal or snack consumed. In some embodiments, causing insulin to be delivered to the patient can include transmitting an instruction that actuates an infusion pump to deliver a dose of insulin determined based on the CIR.

[0061] As above combined Figure 2B As described, the CIR determined using the techniques disclosed herein may be different from the CIR determined using conventional techniques, such as the CIR determined using the 500 rule, the 350 rule, and / or the 300 rule. In particular, the CIR may vary at relatively low TDDs (e.g., less than about 35 units) and relatively high TDDs (e.g., greater than about 85 grams / unit, greater than about 95 grams / unit, etc.). As a more specific example, using the techniques disclosed herein, a CIR of less than 25 grams / unit may be determined for a TDD of 10 units or less, while a CIR for a TDD of 10 units may be conventionally determined to be greater than 25 grams / unit (e.g., as determined using the 500 rule, the 350 rule, or the 300 rule). As another more specific example, using the techniques disclosed herein, a CIR of greater than about 12 grams / unit may be determined for a TDD of 25 units or less, while a CIR for a TDD of 25 units may be conventionally determined to be greater than 12 grams / unit. As another more specific example, using the techniques disclosed herein, the CIR for TDD in the range of approximately 85 to 95 units may be determined to be greater than 5 grams / unit, while the CIR may be stabilized at 5 grams / unit when using the 500 rule, 350 rule, or 300 rule.

[0062] Figure 4 4 is a flow chart of an exemplary process 400 for determining at least one CIR of a patient using an obtained TDD according to some embodiments. The blocks of process 400 may be executed by one or more processors or controllers (such as a controller of a delivery device, a monitoring device, a computing device or system, etc.). Figure 1 An example of such an apparatus is shown and described. In some embodiments, the blocks of process 400 may be executed in a manner different from that of Figure 4In some embodiments, two or more blocks of process 400 may be performed substantially in parallel. In some embodiments, one or more blocks of process 400 may be omitted.

[0063] Process 400 may begin at 410 by obtaining the TDD of a patient's insulin. The TDD may be obtained through user input, e.g., by the patient, by a healthcare provider, etc. The TDD may be obtained upon initialization or setup of a medical device (e.g., initialization of an infusion pump).

[0064] At 420, process 400 may determine at least one CIR for the patient, wherein for a TDD of less than 10 units, the CIR is less than about 25 grams / unit, or wherein for a TDD of less than about 105 units, the CIR is greater than about 5 grams / unit. In other words, in the case where the TDD obtained at box 410 is less than about 10 units, the CIR determined at box 420 is less than about 25 grams / unit, and in the case where the TDD obtained at box 410 is less than 105 units, the CIR determined at box 420 is greater than about 5 grams / unit. In some embodiments, the CIR may be different from the CIR that would be predicted for other TDDs by the 500 rule, the 350 rule, or the 300 rule. For example, in the case where the TDD obtained at box 410 is less than about 25 units, the CIR may be less than about 13 grams / unit. As another example, in the case where the TDD obtained at box 410 is less than about 15 units, the CIR may be less than about 20 grams / unit.

[0065] Optionally, at 430, process 400 can cause insulin to be delivered to the patient based at least in part on the CIR determined at 420. For example, in some embodiments, process 400 can determine an insulin dose (e.g., a bolus insulin dose) based on the CIR, e.g., in response to an indication of the number of carbohydrates in a meal or snack consumed. In some embodiments, causing insulin to be delivered to the patient can include transmitting an instruction that actuates an infusion pump to deliver a dose of insulin determined based on the CIR.

[0066] Exemplary Devices

[0067] In some embodiments, a therapy may be administered based on a determination to transmit the therapy toward a therapy delivery device. Figure 5 Describing a non-limiting example of such a device, Figure 5 An exemplary insulin delivery device 500 is depicted in accordance with aspects of the present disclosure.

[0068] As described above, therapy determinations may be communicated toward the insulin delivery device 500 (e.g., from the cloud computing system 108, via an intermediate computing device 106 communicatively coupled to the device 500). The insulin delivery device 500 may be an example of a delivery device 102 as described throughout this disclosure. In such a device, insulin delivery may be performed based on internal communications between a central computing module (e.g., a microcontroller for the entire device 500) and an insulin delivery module (e.g., including a motor and a 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 500) to communicate with the insulin delivery module. Figure 1 108) computing device (e.g., Figure 1 In some implementations, the insulin delivery device 500 can transmit various event data (e.g., meal data, exercise data, and / or insulin delivery data) to a remote or cloud computing system, and the remote or cloud computing system can transmit insulin delivery determinations to the insulin delivery device 500. Figure 6 Components that may be included in the insulin delivery device 500 are also shown.

[0069] The insulin delivery device 500 can provide fast-acting insulin through a small tube 510, which is configured to be fluidly connected to a cannula (not shown). The cannula can be inserted subcutaneously under a fixed dressing 540, which includes an inlet for the small tube 510, an outlet for the cannula, and an adhesive surface for fixing the dressing 540 to the skin. The device 500 can deliver at least 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, which covers the blood sugar increase caused by a meal and / or otherwise corrects a high blood sugar level. The depicted insulin delivery device 500 includes a user interface with a button element 520, which can be manipulated to administer an insulin bolus, change a therapy setting, change a user preference, and select display features, etc. The insulin delivery device 500 also includes a display device 530 that can be used to present various types of information or data (such as notifications or alarms of the above types) to the user. According to aspects of the present disclosure, a user of the insulin delivery device 500 may use the button element 520 to input certain event data (eg, event type, event start time, event details, etc.), and may use the display device 530 to confirm the user input. Figure 5 The depicted insulin delivery device 500 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.

[0070] Figure 6 is a block diagram of an embodiment of a computer system 600 that may be utilized in embodiments as described herein. It should be noted that Figure 6 It is intended only to provide a generalized illustration of the various components, any or all of which may be utilized as appropriate. Figure 6 The components shown may be localized to a single device (eg, delivery device 102, monitoring device 104, computing device 106, or computing system 108) and / or distributed among various networked devices that may be located at different geographic locations.

[0071] In some embodiments, the computer system 600 may include hardware elements that may be electrically coupled via a bus (or may communicate in other ways as appropriate). The hardware elements may include a processor 610, which may include, but is not limited to, one or more microcontrollers, one or more microprocessors, one or more general-purpose processors, one or more special-purpose processors (such as digital signal processing chips, graphics acceleration processors, etc.), and / or other processing structures that may be configured to perform one or more of the methods or functions described herein.

[0072] In some embodiments, the computer system 600 may further include one or more input devices 615, which may include but are not limited to a button element 520, a microphone, a glucose sensor, etc. The computer system 600 may further include one or more output devices 620, which may include but are not limited to a display device (e.g., 530), a speaker, a buzzer, etc.

[0073] In some embodiments, the computer system 600 may also include one or more non-transitory storage devices 625, which may include, but are not limited to, local and / or network accessible storage devices, and / or may include, but are not limited to, disk drives, drive arrays, optical storage devices, solid-state storage devices, such as programmable flash-updatable read-only memory ("RAM") and / or read-only memory ("ROM"), etc. Such storage devices may be configured to implement any suitable data storage, including, but not limited to, various file systems, database structures, etc. Such data storage may include databases and / or other data structures for storing and managing messages and / or other information to be sent to one or more other components or external devices.

[0074] In some embodiments, the computer system 600 may also include a communication subsystem 630 that may implement wireless communication technology managed and controlled by the wireless communication interface 633. Additionally or alternatively, the communication subsystem 630 may implement wired technology (such as Ethernet, coaxial communication, universal serial bus (USB), etc.). The wireless communication interface 633 may include one or more wireless transceivers that may send and receive wireless signals (e.g., signals based on Bluetooth, Bluetooth low energy (BLE)). Therefore, the communication subsystem 630 may include a modem, a network card (wireless or wired), an infrared communication device, a wireless communication device and / or a chipset, etc., which may enable the computer system 600 to communicate with relevant Figure 1 The communication subsystem 630 may communicate with any of the devices discussed herein, including the delivery device 102, the monitoring device 104, the computing device 106, and / or the cloud computing system 108 as described herein. Thus, the communication subsystem 630 may be used to receive and send data (e.g., SG, Ip, insulin delivery information) as described in the embodiments herein.

[0075] In some embodiments, the computer system 600 will also include a working memory 635, which may include a RAM or ROM device, as described above. The software elements shown as being located within the working memory 635 may include computer-readable and computer-executable instructions 640; device drivers; executable libraries; and / or other codes, which may include computer programs used in various embodiments and / or may be designed to implement the methods and / or configuration systems according to the embodiments described herein. By way of example only, one or more operations described with respect to the methods or functions discussed above may be implemented as codes and / or instructions that can be executed by a computer (and / or a processor within a computer). Such codes and / or instructions may be used to configure and / or adapt a general-purpose computer (or other device) to perform one or more operations according to the described methods.

[0076] In some embodiments, these instruction sets and / or code sets may be stored on a non-transitory computer-readable storage medium (such as the storage device 625 described above). In some cases, the storage medium may be incorporated into a computer system (such as computer system 600). In other embodiments, the storage medium may be separate from the computer system (e.g., removable media such as a compact disc), and / or provided in a downloadable installation package so that the storage medium can be used to program, configure, and / or adapt a general-purpose computer having the instructions and / or code stored thereon. These instructions may take the form of executable code that can be executed by the computer system 600, and / or may take the form of source code and / or installable code that takes the form of executable code when compiled and / or installed on the computer system 600 (e.g., using any of a variety of commonly available compilers, installers, compression / decompression utilities, etc.).

[0077] Each embodiment disclosed herein is an example 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. 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 reference numerals may refer to similar elements.

[0078] Any of the techniques, operations, methods, programs, algorithms or codes described herein may be converted to 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 for specifying 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, PL1, Python, script processing language, visual Basic, metalanguage of self-specified program, and all first generation, second generation, third generation, fourth generation, fifth generation or higher generation computer languages. Also included are databases and other data patterns and any other metalanguages. No distinction is made between interpretation, compiled languages ​​or languages ​​using compilation and interpretation methods simultaneously. No distinction is made between the 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.

[0079] 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.

[0080] 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.

[0081] Exemplary embodiments

[0082] Embodiment 1: A method for determining parameters of a medical device, the method comprising: obtaining a measure of a patient's insulin dosage; and determining at least one carbohydrate to insulin ratio for the patient based at least in part on a model and using the obtained measure of the patient's insulin dosage, wherein the parameters of the model are determined based on data associated with a patient population.

[0083] Embodiment 2: The method of embodiment 1, wherein the measure of the insulin dose is at least one of: a total daily dose (TDD) of insulin, a total bolus dose of insulin, or a total dose over a predetermined period of time.

[0084] Embodiment 3: A method according to any of the preceding embodiments, wherein for a given patient in the patient population, the data associated with the patient population includes an insulin dosage metric associated with the given patient and at least one carbohydrate to insulin ratio used by the patient.

[0085] Embodiment 4: The method according to any one of the preceding embodiments, wherein the patient population comprises a group of patients having a similar demographic group as the patient.

[0086] Embodiment 5: The method according to embodiment 4, wherein the similar demographic group includes the group of patients having a similar age as the patient.

[0087] Embodiment 6: The method according to any of the preceding embodiments, wherein the model comprises one or more mathematical functions representing a carbohydrate to insulin ratio that decreases at a rate proportional to its current value.

[0088] Embodiment 7: A method according to embodiment 6, wherein the one or more mathematical functions approximate an exponential decay.

[0089] Embodiment 8: The method according to any one of the preceding embodiments, further comprising providing an indication of a range of carbohydrate to insulin ratios spanning a predetermined confidence interval based on the data associated with the patient population.

[0090] Embodiment 9: The method of embodiment 8, wherein the indication of the range of carbohydrate to insulin ratio is provided when initializing or modifying use of an insulin infusion pump.

[0091] Embodiment 10: A method according to any of the preceding embodiments, wherein obtaining the measure of the patient's insulin dosage is during operation of an insulin infusion pump, and wherein the determined at least one carbohydrate to insulin ratio is used to modify programming of the insulin infusion pump.

[0092] Embodiment 11: A method according to any of the preceding embodiments, wherein the at least one carbohydrate to insulin ratio of the patient is determined at least in part based on an operating mode of an insulin infusion pump using the at least one carbohydrate to insulin ratio, and wherein the operating mode of the insulin infusion pump includes at least one of a manual mode or a fully closed-loop mode.

[0093] Embodiment 12: The method according to any one of the preceding embodiments, wherein the at least one carbohydrate to insulin ratio of the patient comprises a plurality of carbohydrate to insulin ratios, each carbohydrate to insulin ratio being applicable to a predetermined time of day.

[0094] Embodiment 13: 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 measure of a patient's insulin dosage; and determining at least one carbohydrate-to-insulin ratio for the patient based at least in part on a model and using the obtained measure of the patient's insulin dosage, wherein parameters of the model are determined based on data associated with a patient population.

[0095] Embodiment 14: The system of Embodiment 13, wherein the model comprises one or more mathematical functions that represent a carbohydrate to insulin ratio that decreases at a rate proportional to its current value.

[0096] Embodiment 15: One or more processor-readable media storing instructions that, when executed by one or more processors, result in the following operations: obtaining a measure of a patient's insulin dosage; and determining at least one carbohydrate-to-insulin ratio for the patient based at least in part on a model and using the obtained measure of the patient's insulin dosage, wherein parameters of the model are determined based on data associated with a patient population.

[0097] Embodiment 16: A method of determining a medical device parameter, the method comprising: obtaining a total daily dose (TDD) of insulin for a patient; and determining at least one carbohydrate to insulin ratio for the patient, wherein for a TDD of less than 10 units, the carbohydrate to insulin ratio is less than 25 grams / unit, and wherein for a TDD of less than 105 units, the carbohydrate to insulin ratio is greater than 5 grams / unit.

[0098] Embodiment 17: The method of Embodiment 16, wherein the at least one carbohydrate to insulin ratio is obtained from a lookup table.

[0099] Embodiment 18: The method of Embodiment 17, wherein the lookup table comprises a plurality of potential carbohydrate to insulin ratios for a given TDD, each potential carbohydrate to insulin ratio being applicable to a different percentile of a group of patients used to generate the lookup table.

[0100] Embodiment 19: The method of embodiment 18, further comprising providing alerts or indications applicable to different percentiles of carbohydrate to insulin ratios.

[0101] Embodiment 20: The method according to any one of Embodiments 18 or 19, wherein the group of patients includes patients having a similar demographic group as the patient.

[0102] Embodiment 21: The method of any one of Embodiments 16 to 20, further comprising causing insulin to be delivered to the patient based at least in part on the determined at least one carbohydrate to insulin ratio.

Claims

1. A method for determining a parameter of a medical device, the method comprising: obtain a measure of the patient's insulin dosage; as well as At least one carbohydrate-to-insulin ratio for the patient is determined based at least in part on a model and using the obtained measure of the patient's insulin dosage, wherein parameters of the model are determined based on data associated with a patient population. 2 . The method of claim 1 , wherein the measure of insulin dosage is at least one of: a total daily dose (TDD) of insulin, a total bolus dose of insulin, or a total dose over a predetermined period of time.

3. The method of claim 1, wherein for a given patient in the patient population, the data associated with the patient population includes an insulin dosage metric associated with the given patient and at least one carbohydrate to insulin ratio used by the patient.

4. The method of claim 1, wherein the patient population comprises a group of patients having a similar demographic group as the patient.

5. The method of claim 4, wherein the similar demographic group comprises the group of patients having a similar age as the patient.

6. The method of claim 1, wherein the model comprises one or more mathematical functions that represent a carbohydrate to insulin ratio that decreases at a rate proportional to its current value. The method of claim 6 , wherein the one or more mathematical functions approximate an exponential decay.

8. The method of claim 1, further comprising providing an indication of a range of carbohydrate to insulin ratios spanning a predetermined confidence interval based on the data associated with the patient population.

9. The method of claim 8, wherein the indication of the range of carbohydrate to insulin ratio is provided when initializing or modifying use of an insulin infusion pump.

10. The method of claim 1, wherein obtaining the measure of the patient's insulin dosage is during operation of an insulin infusion pump, and wherein the determined at least one carbohydrate to insulin ratio is used to modify programming of the insulin infusion pump.

11. The method of claim 1 , wherein the at least one carbohydrate to insulin ratio of the patient is determined based at least in part on a mode of operation of an insulin infusion pump that uses the at least one carbohydrate to insulin ratio, and wherein the mode of operation of the insulin infusion pump comprises at least one of a manual mode or a full closed loop mode.

12. The method of claim 1, wherein the at least one carbohydrate to insulin ratio of the patient comprises a plurality of carbohydrate to insulin ratios, each carbohydrate to insulin ratio being applicable to a predetermined time of day.

13. 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 measure of the patient's insulin dosage; and At least one carbohydrate-to-insulin ratio for the patient is determined based at least in part on a model and using the obtained measure of the patient's insulin dosage, wherein parameters of the model are determined based on data associated with a patient population.

14. The system of claim 13, wherein the model includes one or more mathematical functions that represent a carbohydrate to insulin ratio that decreases at a rate proportional to its current value.

15. A method for determining a parameter of a medical device, the method comprising: Obtain the patient's total daily dose (TDD) of insulin; as well as At least one carbohydrate to insulin ratio is determined for the patient, wherein for a TDD of less than 10 units, the carbohydrate to insulin ratio is less than 25 grams / unit, and wherein for a TDD of less than 105 units, the carbohydrate to insulin ratio is greater than 5 grams / unit.

16. The method of claim 15, wherein the at least one carbohydrate to insulin ratio is obtained from a lookup table.

17. The method of claim 16, wherein the lookup table comprises a plurality of potential carbohydrate to insulin ratios for a given TDD, each potential carbohydrate to insulin ratio being applicable to a different percentile of a group of patients used to generate the lookup table.

18. The method of claim 17, wherein the group of patients includes patients having a similar demographic group as the patient.

19. The method of claim 17, further comprising providing alerts or indications applicable to different percentiles of carbohydrate to insulin ratios.

20. The method of claim 15, further comprising causing insulin to be delivered to the patient based at least in part on the determined at least one carbohydrate to insulin ratio.

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