Patent Application
20230372613
An automated insulin delivery (AID) device delivers small amounts of insulin to a diabetic user to help regulate the glucose levels of the user. Typically, the small amounts of insulin are delivered at periodic intervals, such as at five-minute intervals. A control system in the AID device may determine the dosage of insulin to deliver at each interval. The control system may look at a number of different factors to determine the dosage of insulin to deliver. These factors may include a predicted future glucose level for the user. The control system may employ a glucose prediction model (GPM) that determines the predicted future glucose level for the user. The control system may compare the predicted future glucose level from the GPM with a target glucose level to determine a difference. The difference may dictate, in part, the insulin dosage delivered for the next or upcoming cycles. For example, if the GPM predicts that the glucose level of the user is going to be substantially above target, the control system may increase the insulin dosage delivered in the next cycle.
The GPM predicts the future glucose level well for many users. However, for users that are insulin sensitive or insulin insensitive, the GPM may not make very accurate predictions. This may be problematic in that the control system may be making decisions regarding insulin dosages based on inaccurate information. Moreover, there is an increased risk of poor glucose level control as a result of the inaccurate predicted future glucose levels that are predicted by the GPM.
In accordance with a first inventive facet, an insulin delivery device includes a reservoir for storing insulin and a non-transitory storage medium for storing computer programming instructions and past glucose levels for a user of the insulin delivery device. The insulin delivery device further includes a processor for executing the computer programming instructions to cause the processor to customize a glucose prediction model for the user for predicting future glucose levels of the user based on the past glucose level readings for the user and use the customized glucose prediction model in determining a basal insulin delivery dosage by the insulin delivery device. The computer programming instructions further cause the processor to cause the delivery of the determined basal insulin delivery dosage from the reservoir to the user.
The processor may be further configured to modify the glucose prediction model in view of more recent past glucose levels for the user and use the modified glucose prediction model in determining a next basal insulin delivery dosage by the insulin delivery device. The processor may be further configured to update the customizing of the glucose prediction model based on glucose levels received since the customizing, use the updated customized glucose prediction model in determining a new basal insulin delivery dosage by the insulin delivery device, and cause the insulin delivery device to deliver the determined new basal insulin delivery dosage. The customizing of the glucose prediction model may choose weight coefficient values used in the glucose prediction model. The customizing may entail using linear regression analysis to choose coefficient values that substantially minimize an error between predicted glucose levels that are predicted from past glucose levels for the user and corresponding actual glucose level readings for the user. The glucose prediction model may be linear. The glucose prediction model may ignore how much insulin has been delivered to the user.
In accordance with another inventive facet, a method is performed by a processor of an electronic device. The method includes determining values of weights for past glucose levels of a user of an insulin delivery device based on a glucose history for the user and applying the determined weights to the past glucose levels to produce weighted past glucose levels. The method also includes determining a predicted glucose level for a user at a given time as a sum of the weighted past glucose levels and using the predicted glucose level for the user to control delivery of insulin to the user by the insulin delivery device.
The determining of the values of the weights for the past glucose levels of the user of the insulin delivery device based on the glucose history for the user may entail, for selected glucose levels in the glucose history that includes glucose levels and associated times at which the glucose levels were sensed, calculating predicted glucose levels from weighted glucose levels in the glucose history for times that immediately precede the times of the selected glucose levels in the glucose history. The determining of the values of the weights may entail performing least squares regression analysis with the past glucose levels and predicted glucose levels that are predicted from the past glucose levels. A predicted glucose level may be calculated as a sum of the weighted glucose levels in the glucose history for times that immediately precede a time of the given one of the predicted glucose levels. The method may further include comparing the predicted glucose level to a high glucose level threshold and where the predicted glucose level exceeds the high glucose level threshold, taking corrective action. The corrective action may include one or more of outputting an alert, outputting a recommendation or delivering an insulin bolus to the user. The method may further include comparing the predicted glucose level to a low glucose level threshold and where the predicted glucose level falls below the low glucose level threshold, taking corrective action. The corrective action may include one or more of outputting an alert, outputting a recommendation to ingest rescue carbohydrates or delivering a glucagon bolus to the user.
In accordance with a further inventive facet, an electronic device includes a storage for storing computer programming instructions for controlling operation of a insulin delivery device and a processor for executing the computer programming instructions. The computer programming instructions are for causing the processor to use a glucose prediction model to predict future glucose levels of a user of the insulin delivery device and to customize the glucose prediction model of the user based on past glucose levels of the user. The computer programming instructions also are for causing the processor to use the customized glucose prediction model to predict future glucose levels of the user and use at least one of the predicted future glucose levels in determining a basal delivery dosage of insulin to be delivered to the user from the insulin delivery device.
The electronic device may be the insulin delivery device or a management device of the insulin delivery device. The computer programming instructions may include instructions for causing the processor to update the customizing of the glucose prediction model based on more recent glucose levels for the user. The computer programming instructions may include instructions for causing the processor to adjust the predicted glucose levels for the user to account for noise. The glucose prediction model may not account for insulin delivered to the user in predicting the future glucose levels of the user.
The exemplary embodiments may employ a GPM that is tailored to a user to account for insulin sensitivity or insulin insensitivity. The Exemplary embodiments provide an approach to adapt the parameters of a GPM in real time based on least squares regression with regularization of glucose predictions versus actual glucose values. The exemplary embodiments may predict future glucose levels based on past glucose levels of the user. Specifically, the GPM in exemplary embodiments may predict the future glucose level of the user as a weighted sum of most recent glucose levels from the user (such as the most recent glucose level readings from a glucose monitor). The exemplary embodiments may employ linear regression analysis to determine the values of the weights. These weights customize the GPM of the user based on the user's most recent glucose level history. Due to the customization, the GPM may more accurately predict future glucose levels of the user. As a result, the AID may exhibit better glucose level control for the user.
The GPM of the exemplary embodiments may be updated on an ongoing basis. As new glucose level readings arrive, the weights may be updated to reflect the more recent glucose level readings for the user.
The GPM may also be updated so as to minimize the effect of noise. The exemplary embodiments may limit the amount of change in weights between updates so as to avoid more significant changes that may be the result of noisy glucose level readings for the user. This approach changes more slowly as a result but avoids the complication of noise.
The GPM is a model for predicting the future glucose levels of the user. The GPM need not be a formalized model but rather refers to a strategy for predicting the future glucose levels. The GPM may be a simple linear equation or even a heuristic in some instances. The approach adopted by the GPM may be non-linear in alternative embodiments.
The medicament delivery device 102 may include a processor 110. The processor 110 may be, for example, a microprocessor, a logic circuit, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC) or a microcontroller. The processor 110 may maintain a date and time as well as other functions (e.g., calculations or the like). The processor 110 may be operable to execute a control application 116 encoded in computer programming instructions stored in the storage 114 that enables the processor 110 to direct operation of the medicament delivery device 102. The control application 116 may be realized as a single program, multiple programs, modules, libraries, or the like. The control application 116 may be responsible for implementing the control system that provides feedback and adjustments to medicament dosages that are delivered to the user 108. The control application 116, in some exemplary embodiments, may implement the GPM 116′ and provide the functionality detailed below. The processor 110 also may execute computer programming instructions stored in the storage 114 for a user interface 117 that may include one or more display screens shown on display 109. The display 109 may display information to the user 108 and, in some instances, may receive input from the user 108, such as when the display 109 is a touchscreen.
The control application 116 may control delivery of a medicament to the user 108 per a control approach like that described herein. The storage 114 may hold histories 111 for the user 108, such as a history of basal deliveries, a history of bolus deliveries, and/or other histories, such as a meal event history, exercise event history, glucose level history, medicament delivery history, sensor data history and/or the like. These histories 111 may be processed as will be described below to adjust basal medicament dosages to help reduce or eliminate persistent positive low level medicament excursions. The storage 114 also may include one or more basal profiles 115 that are used when the medicament delivery device is operating in open loop mode. In addition, the processor 110 may be operable to receive data or information. The storage 114 may include both primary memory and secondary memory. The storage 114 may include random access memory (RAM), read only memory (ROM), optical storage, magnetic storage, removable storage media, solid state storage or the like.
The medicament delivery device 102 may include one or more housings for housing its various components including a pump 113, a power source (not shown), and a reservoir 112 for storing a medicament for delivery to the user 108. The medicament in the reservoir 112 may be insulin, for example, or the other medicaments noted above. In some embodiments, the reservoir may be partitioned to store another medicament as well, such as glucagon, or one of the other medicaments noted above. A fluid path to the user 108 may be provided, and the medicament delivery device 102 may expel the medicament from the reservoir 112 to deliver the medicament to the user 108 using the pump 113 via the fluid path. The fluid path may, for example, include tubing coupling the medicament delivery device 102 to the user 108 (e.g., tubing coupling a cannula to the reservoir 112) and may include a conduit to a separate infusion site.
There may be one or more communications links with one or more devices physically separated from the medicament delivery device 102 including, for example, a management device 104 of the user 108 and/or a caregiver of the user 108, a sensor 106, a smartwatch 130, a fitness monitor 132 and/or another variety of wearable device 134. The communication links may include any wired or wireless communication links operating according to any known communications protocol or standard, such as Bluetooth®, Wi-Fi, a near-field communication standard, a cellular standard, or any other wireless protocol.
The medicament delivery device 102 may interface with a network 122 via a wired or wireless communications link. The network 122 may include a local area network (LAN), a wide area network (WAN) or a combination therein. A computing device 126 may be interfaced with the network, and the computing device may communicate with the medicament delivery device 102 or the management device 104.
The medicament delivery system 100 may include one or more sensor(s) 106 for sensing the levels of one or more analytes or for sensing environmental conditions. Examples of sensors 106 include a continuous glucose monitor (CGM), a hear rate monitor, a blood pressure monitor, a temperature sensor, a barometer, an accelerometer, etc. The sensor(s) 106 may be coupled to the user 108 by, for example, adhesive or the like and may provide information or data on one or more medical conditions and/or physical attributes of the user 108. The sensor(s) 106 may be physically separate from the medicament delivery device 102 or may be an integrated component thereof.
The medicament delivery system 100 may or may not also include management device 104. In some embodiments, a management device is not needed as the medicament delivery device 102 may manage itself. The management device 104 may be a special purpose device, such as a dedicated personal diabetes manager (PDM) device. The management device 104 may be a programmed general-purpose device, such as any portable electronic device including, for example, a dedicated controller, such as a processor, a micro-controller, or the like. The management device 104 may be used to program or adjust operation of the medicament delivery device 102 and/or the sensors 106. The management device 104 may be any portable electronic device including, for example, a dedicated device, a smartphone, a smartwatch or a tablet. In the depicted example, the management device 104 may include a processor 119 and a storage 118. The processor 119 may execute processes to manage a user's glucose levels and to control the delivery of the medicament to the user 108. The medicament delivery device 102 may provide data from the sensors 106 and other data to the management device 104. The data may be stored in the storage 118. The processor 119 may also be operable to execute programming code stored in the storage 118. For example, the storage 118 may be operable to store one or more control applications 120 for execution by the processor 119. The control application 120 may be responsible for controlling the medicament delivery device 102, such as by controlling the AID delivery of insulin to the user 108. The control application 120 may implement the GPM 120′ in some embodiments. The control application 120 may customize the GPM 120′ and implement the functionality described below. The storage 118 may store the control application 120, histories 121 like those described above for the medicament delivery device 102, one or more basal profiles 135 and other data and/or programs.
A display 127, such as a touchscreen, may be provided for displaying information. The display 127 may display user interface (UI) 123. The display 127 also may be used to receive input, such as when it is a touchscreen. The management device 104 may further include input elements 125, such as a keyboard, button, knobs, or the like, for receiving input form the user 108.
The management device 104 may interface with a network 124, such as a LAN or WAN or combination of such networks via wired or wireless communication links. The management device 104 may communicate over network 124 with one or more servers or cloud services 128. Data, such as sensor values like glucose levels, may be sent, in some embodiments, for storage and processing from the medicament delivery device 102 directly to the cloud services/server(s) 128 or instead from the management device 104 to the cloud services/server(s) 128. The cloud services/server(s) 128 may provide output from the model 115 as needed to the management device 104 and/or medicament delivery device 102 during operation.
Other devices, like smartwatch 130, fitness monitor 132 and wearable device 134 may be part of the medicament delivery system 100. These devices 130, 132 and 134 may communicate with the medicament delivery device 102 and/or management device 104 to receive information and/or issue commands to the medicament delivery device 102. These devices 130, 132 and 134 may execute computer programming instructions to perform some of the control functions otherwise performed by processor 110 or processor 119, such as via control applications 116 and 120. These devices 130, 132 and 134 may include displays for displaying information. The displays may show a user interface for providing input by the user 108, such as to request a change or pause in dosage or to request, initiate, or confirm delivery of a bolus of a medicament, or for displaying output, such as a change in dosage (e.g., of a basal delivery amount) as determined by processor 110 or management device 104. These devices 130, 132 and 134 may also have wireless communication connections with the sensor 106 to directly receive analyte measurement data.
The functionality described below for the exemplary embodiments may be under the control of or performed by the control application 116 of the medicament delivery device 102 or the control application 120 of the management device 104. In some embodiments, the functionality may be under the control of or performed by the cloud services or servers 128, the computing device 126 or by the other enumerated devices, including smartwatch 130, fitness monitor 132 or another wearable device 134.
The medicament delivery device 102 may operate in an open loop mode and in a closed loop mode. In the open loop mode, the user 108 manually inputs the amount of medicament to be delivered (such as per hour) for segments of the day. The inputs may be stored in a basal profile 115, 135 for the user 108. In other embodiments, a basal profile may not be used. The control application 116, 120 uses the input information from the basal profile 115, 135 to control basal medicament deliveries in open loop mode. In contrast, in the closed loop mode, the control application 116, 120 determines the medicant delivery amount for the user 108 on an ongoing basis based on a feedback loop. For an insulin delivery device, the aim of the closed loop mode is to have the user's glucose level at a target glucose level or within a range of glucose levels. The basal dosages may be delivered at fixed regular intervals, designated as cycles, such as every five minutes, though may vary in amount for each cycle. The GPM 116′ or 120′ is used in closed loop mode.
In the exemplary embodiments, the functionality described below may be realized by executing the control application 116 or 120 or by running a control application on other devices, such as smartwatch 130, fitness monitor 132 or other type of wearable device 134. More generally, the functionality may be realized by computer programming instructions executing on a processor for controlling the medicament delivery device 102.
The customization and use of the GPM described below may be performed by control application 116 of the medicament delivery device 102 (i.e., the AID device) or by the control application 120 of the management device 104. Some functionality and/or operations may be performed by the smartwatch 130, the fitness monitor 132, the other wearable device 134, the computing device 126 and/or the cloud services/server 128 in some embodiments.
The exemplary embodiments may more accurately predict future glucose levels for a user 108 than conventional AID devices.
G(k)=b*1G(k−1)+b*2G(k−2)+b*3G(k−3)
where b* is a weight, also referred to as a weight coefficient, where b*1, b*2, and b*3 represent different weight coefficients for different cycles. A cycle refers to an operational cycle of the medicament delivery device 102. Each cycle may last a fixed period of time, such as 5 minutes, and a basal delivery dosage may be determined for each cycle. Although this example assesses the value of current glucose concentration across three previous cycles (applying an equation for up to G(k−3)), this formulation can be extended into a greater number of previous cycles, each additional cycle having its own weight b* such as b*4, b*5 . . . etc.
For the previous cycle k−1, the equation is:
G(k−1)=b*1G(k−2)+b*2G(k−3)+b*3G(k−4).
M+1 of these equations may be used in the customization. M is an integer and is a customizable value. At 304, the subset of previous glucose levels that are used in predicting an associated glucose level are gathered into a matrix G with one subset per row. As above, these equations can also be extended further to additional previous cycles beyond G(k−4).
As shown in
Given this formulation for b,
Since in the example G is a M×3 matrix, GTG results in a 3×3 matrix, as is the inverse. When the inverse is multiplied by GT, a 3×M matrix, the result is a 3×M matrix. When this matrix is multiplied by the matrix g, which is a M×1 matrix, the result is a 3×1 matrix for b. Note that the sizes of these matrices can be varied based on the model order of prediction. Specifically, if the duration of previous cycles is increased from 3 to N, the example G matrix may be an M×N matrix, and the result of the multiplication with GT will be an N×N matrix. The final outcome will be an N×1 matrix for b, corresponding to the weights of historic glucose data samples.
The least squares weights b may be recalculated periodically, e.g., every 6 or 24 hours, they may be recalculated when triggered by certain events as described below, or they may be continuously updated per each cycle of operation.
As was mentioned above, the GPM 116′ or 120′ may be updated to reflect more recent glucose level data for the user 108.
The trigger instead may be a time based trigger 710. For example, a new cycle beginning 712 (i.e., every 5 minutes) may trigger an update to the customization of the GPM 116′ or 120′. A new hourly interval 714 may trigger an update. For instance, an update may occur every hour, every 3 hours or every 12 hours. A new day 716 may trigger an update. It should be appreciated that other time intervals may be used to trigger the updates.
If triggered at 602, at 604, updated glucose level data is accessed. For instance several new glucose level readings may have been received from the sensor(s) 106. At 606, the GPM 116′ or 120′ is updated in response to the trigger to account the new glucose level readings. At 608, the updated GPM 116′ or 120′ is used to predict at least one future glucose level for the user 108.
The predicted glucose level for the user from the GPM 116′ or 120′ may be used in a number of different ways.
Another action that may result from the customization of the GPM 116′ or 120′ is an adjustment is the basal delivery dosage from the medicament delivery device 102.
One problem that may arise with the customization of the GPM 116′ or 120′ is that one or more noisy glucose level reading from sensor 106 may have an undue effect on the weights in matrix b. Hence, steps may be taken to reduce the effects of the noise by only making incremental changes so that the effects of noise are minimized.
B
final=0.9bfinal(k−1)+0.1b*(k−1).
The present disclosure furthermore relates to computer programs comprising instructions (also referred to as computer programming instructions) to perform the aforementioned functionalities. The instructions may be executed by a processor. The instructions may also be performed by a plurality of processors for example in a distributed computer system. The computer programs of the present disclosure may be for example preinstalled on, or downloaded to the medicament delivery device 102, e.g. the storage 114, or on the management device 104, e.g. the storage 118.
While exemplary embodiments have been described herein, it should be appreciated that various in form and detail may be made without departing from the intended scope as defined in the appended claims.
This application claims the benefit of U.S. Provisional Patent Application No. 63/343,739, filed May 19, 2022, the entire contents of which are incorporated herein by reference in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63343739 | May 2022 | US |
