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WO2013074289A2 - Détermination d'un programme de dosage du glucose - Google Patents

Détermination d'un programme de dosage du glucose Download PDF

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Publication number
WO2013074289A2
WO2013074289A2 PCT/US2012/062631 US2012062631W WO2013074289A2 WO 2013074289 A2 WO2013074289 A2 WO 2013074289A2 US 2012062631 W US2012062631 W US 2012062631W WO 2013074289 A2 WO2013074289 A2 WO 2013074289A2
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WO
WIPO (PCT)
Prior art keywords
analyte
user
control chart
population
testing schedule
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2012/062631
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English (en)
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WO2013074289A3 (fr
Inventor
Erwin Satrya Budiman
Kenneth J. Doniger
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Abbott Diabetes Care Inc
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Abbott Diabetes Care Inc
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Filing date
Publication date
Application filed by Abbott Diabetes Care Inc filed Critical Abbott Diabetes Care Inc
Publication of WO2013074289A2 publication Critical patent/WO2013074289A2/fr
Anticipated expiration legal-status Critical
Publication of WO2013074289A3 publication Critical patent/WO2013074289A3/fr
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • glucose or other analytes such as lactate, oxygen or the like
  • the monitoring of glucose is particularly important to individuals with diabetes. Diabetics may need to monitor glucose levels to determine when insulin is needed to reduce glucose levels in their bodies or when additional glucose is needed to raise the level of glucose in their bodies.
  • Devices have been developed for continuous or automatic monitoring of analytes, such as glucose, in bodily fluid such as in the blood stream or in interstitial fluid.
  • analytes such as glucose
  • Some of these analyte measuring devices are configured so that at least a portion of the devices are positioned below a skin surface of a user, e.g., in a blood vessel or in the subcutaneous tissue of a user.
  • Embodiments of the present disclosure include a computer-implemented method for determining an analyte testing schedule based on a user's analyte control level and clinical risk.
  • the method may also include receiving population data related to a population's analyte concentration.
  • the data related to the population's analyte concentration can also be received at a data processing component.
  • the method may include that the data processing component creates an analyte control chart based upon the received population data.
  • the method may include that the data processing component receives data related to the user's analyte concentration, wherein the data related to a user's analyte concentration is acquired by at least one of user-monitored analyte readings and a continuous analyte monitor.
  • the method can further include that the data processing component determines a probability distribution function of the data related to the user's analyte concentration, wherein the probability distribution function is plotted on the analyte control chart.
  • the method may include that the data processing component identifies at least one of an adequate area and an inadequate area of the analyte control chart.
  • the method can also include that the data processing component determines the analyte testing schedule for the user if an inadequate area of the analyte control chart is identified.
  • Embodiments of the present disclosure may also include an integrated analyte monitoring device assembly.
  • the integrated analyte monitoring device assembly may include an analyte sensor for transcutaneous positioning through a skin layer and maintained in fluid contact with an interstitial fluid under the skin layer during a predetermined time period, the analyte sensor having a proximal portion and a distal portion.
  • the integrated analyte monitoring device assembly may include sensor electronics coupled to the analyte sensor.
  • the sensor electronics of the integrated analyte monitoring device assembly may include a circuit board having a conductive layer and a sensor antenna disposed on the conductive layer.
  • the sensor electronics of the integrated analyte monitoring device may also include one or more electrical contacts provided on the circuit board and coupled with the proximal portion of the analyte sensor to maintain continuous electrical communication.
  • the integrated analyte monitoring device of the present disclosure can also include a data processing component provided on the circuit board and in signal communication with the analyte sensor.
  • the data processing component of the integrated analyte monitoring device assembly may be configured to execute one or more routines for processing signals received from the analyte sensor.
  • the data processing component of the integrated analyte monitoring device assembly can be additionally configured to control the transmission of data associated with the processed signals received from the analyte sensor to a remote location using the sensor antenna in response to a request signal received from the remote location.
  • the data processing component of the integrated analyte monitoring device assembly may be configured to receive population data related to a population's analyte concentration, the data processing unit configured to create an analyte control chart based upon the received population data. Additionally, the data processing component of the integrated analyte monitoring device assembly can be configured to receive data related to the user's analyte concentration, wherein the data related to a user's analyte concentration is acquired by at least one of user-monitored analyte readings and a continuous analyte monitor.
  • the data processing component of the integrated analyte monitoring device assembly may also be configured to determine a probability distribution function of the data related to the user's analyte concentration, wherein the probability distribution function is plotted on the analyte control chart.
  • the data processing component of the integrated analyte monitoring device assembly can be further configured to identify at least one of an adequate area and an inadequate area of the analyte control chart.
  • the data processing component of the integrated analyte monitoring device assembly can be configured to determine the analyte testing schedule for the user if an inadequate area of the analyte control chart is identified.
  • Embodiments of the present disclosure may include an analyte monitoring device.
  • the analyte monitoring device may include a data processing component provided on the circuit board and in signal communication with an analyte sensor and a memory for storing instructions for execution by the data processing component.
  • the memory may include instructions which cause the data processing component of the integrated analyte monitoring device to receive population data related to a population's analyte concentration, and to create an analyte control chart based upon the received population data. Additionally, the memory may include instructions which cause the data processing component of the integrated analyte monitoring device to receive data related to the user's analyte concentration, wherein the data related to a user's analyte concentration is acquired by at least one of user-monitored analyte readings and a continuous analyte monitor.
  • the memory may also include instructions which cause the data processing component of the integrated analyte monitoring device to determine a probability distribution function of the data related to the user's analyte concentration, wherein the probability distribution function is plotted on the analyte control chart.
  • the memory may further include instructions which cause the data processing component of the integrated analyte monitoring device to identify at least one of an adequate area and an inadequate area of the analyte control chart and to determine the analyte testing schedule for the user if an inadequate area of the analyte control chart is identified.
  • FIG. 1 is a health monitor device in accordance with certain embodiments of the present disclosure
  • FIG. 2 is a block diagram of the health monitor device of FIG. 1 in accordance with certain embodiments of the present disclosure
  • FIG. 3 illustrates a block diagram of a health monitoring system in accordance with certain embodiments of the present disclosure
  • FIG. 4 is a graphical representation of a glucose control chart in accordance with certain embodiments of the present disclosure.
  • FIG. 5 is a graphical representation of contour plots of a probability distribution function of a patient's state of glucose control in accordance with certain embodiments of the present disclosure
  • FIG. 6 is a graphical representation of the certainty of a patient's predicted glucose control in the context of clinical risks in accordance with certain embodiments of the present disclosure
  • FIG. 7 is a graphical representation of contour lines from density plots of estimated glucose control from 7 different patients in accordance with certain embodiments of the present disclosure
  • FIG. 8 is a graphical representation of contour lines from density plots of estimated glucose control from 7 different patients, under a relatively stringent blood glucose testing schedule in accordance with certain embodiments of the present disclosure
  • FIG. 9 is a graphical representation of the determination of areas where a given testing schedule for 7 different patients is adequate vs. inadequate in detecting a clinically meaningful change in a clinical risk in accordance with certain embodiments of the present disclosure.
  • FIG. 10 is a flowchart illustrating a method for determining an analyte testing schedule based on a user's analyte control level and clinical risk in accordance with certain embodiments of the present disclosure.
  • Patents, applications and/or publications described herein, including the following patents, applications and/or publications are incorporated herein by reference for all purposes: U.S. Patent Nos. 4,545,382, 4,711,245, 5,262,035, 5,262,305, 5,264,104, 5,320,715, 5,356,786, 5,509,410, 5,543,326, 5,593,852, 5,601,435, 5,628,890, 5,820,551, 5,822,715, 5,899,855, 5,918,603, 6,071,391, 6,103,033, 6,120,676, 6,121,009, 6,134,461, 6,143,164, 6,144,837, 6,161,095, 6,175,752, 6,270,455, 6,284,478, 6,299,757, 6,338,790, 6,377,894, 6,461,496, 6,503,381, 6,514,460, 6,514,718, 6,540,891, 6,560,471, 6,579,690, 6,591,125, 6,592,745,
  • devices, systems, kits and methods for providing compact, low profile, on-body physiological parameter monitoring device (physiological parameters such as for example, but not limited to analyte levels, temperature levels, heart rate, etc), configured for single or multiple use over a predetermined time period, which provide a low profile geometry, effective power management, improved shelf life, and ease and comfort of use including device positioning, and activation.
  • Embodiments include an on-body assembly including a transcutaneously positioned analyte sensor and sensor electronics in a compact, low profile integrated assembly and coupled to an insertion device for deployment.
  • embodiments of the present disclosure relate to methods and devices for detecting at least one analyte such as glucose in body fluid.
  • the present disclosure relates to the continuous and/or automatic in vivo monitoring of the level of an analyte using an analyte sensor.
  • FIG. 1 shows a health monitor device in accordance with one embodiment of the present disclosure.
  • Health monitor device 100 includes a housing 110 with a display unit 120 provided thereon. Also shown in FIG. 1 is a plurality of input buttons 130, each configured to allow the user of the health monitor device 100 to input or enter data or relevant information associated with the operation of the health monitor device 100. For example, the user of the health monitor device may operate the one or more input buttons 130 to enter a calibration code associated with a test strip 160, or other fluid sample reception means, for use in conjunction with the health monitor device 100.
  • the health monitor device 100 may include a blood glucose meter.
  • the test strip 160 for use in conjunction with the health monitor device 100 may be a blood glucose test strip configured to receive a blood sample thereon, in order to determine a blood glucose level of the received blood sample. Additionally, the user may operate the one or more input buttons 130 to adjust time and/or date information, as well as other features or settings associated with the operation of the health monitor device 100.
  • the strip port for receiving the test strip 160 may be integrated with the housing of the health monitor device 100, or alternatively, may be provided in a separate housing or as a separate component that may be physically or electrically coupled to the health monitoring device 100.
  • a component including the strip port may be provided in a separate snap-on type housing which physically snaps onto the housing of the health monitor device 100. Additional information is provided in US Patent No. 7,041,468 issued on May 9, 2006 titled "Blood Glucose Tracking Apparatus and Method" and in US Patent Publication No.
  • input unit 140 which, in one embodiment, may be configured as a jog dial, or the like, and provided on the housing 110 of the health monitor device 100. Also shown in FIG. 1 is a strip port 150 which is configured to receive the test strip 160 (with fluid sample provided thereon) substantially in the direction as shown by the directional arrow 170.
  • microprocessor or a control unit 210 (FIG. 2) of the health monitor device 100 may be configured to determine the associated analyte level in the fluid sample, and display the determined analyte level on the display unit 120.
  • Example embodiments of the present disclosure are directed mainly toward
  • analyte levels that may be determined include, for example, acetyl choline, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glucose, glutamine, growth hormones, hormones, ketones (e.g., ketone bodies), lactate, oxygen, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid stimulating hormone, and troponin.
  • analyte levels include, for example, acetyl choline, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glucose, glutamine, growth hormones, hormones, ketones (e.g., ketone bodies), lactate, oxygen, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid stimulating hormone, and
  • Assays suitable for determining the concentration of DNA and/or RNA are disclosed in U.S. Patent No. 6,281,006 and U.S. Patent No. 6,638,716, the disclosures of each of which are incorporated by reference herein.
  • concentration of drugs such as, for example, antibiotics (e.g., gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, may also be determined.
  • FIG. 2 is a block diagram of the health monitor device of FIG. 1 in one embodiment of the present disclosure.
  • the health monitor device 100 (FIG. 1) includes a controller unit 210 operatively coupled to a communication interface 220 and configured for bidirectional communication.
  • the controller unit 210 is further operatively coupled to a test strip interface 230, an input section 240 (which, for example, may include the input unit 140 and the plurality of input buttons 130 as shown in FIG. 1), an output unit 250, and a data storage unit 260.
  • the test strip interface 230 is configured for signal communication with the inserted test strip 160 (FIG. 1) for determination of the fluid sample on the test strip 160.
  • the test strip interface 230 may include an illumination segment which may be configured to illuminate the strip port 150 (FIG. 1) using a light emitting diode (LED), for example, during the test strip 160 insertion process to assist the user in properly and accurately inserting the test strip 160 into the strip port 150.
  • LED light emitting diode
  • the test strip interface 230 may be additionally configured with a physical latch or securement mechanism internally provided within the housing 110 of the health monitor device with a medication dose calculation function 100 (FIG. 1) such that when the test strip 160 is inserted into the strip port 150, the test strip 160 is retained in the received position within the strip port 150 until the sample analysis is completed.
  • a physical latch or securement mechanism may include a uni-directionally biased anchor mechanism, or a pressure application mechanism to retain the test strip 160 in place by applying pressure on one or more surfaces of the test strip 160 within the strip port 150.
  • the output unit 250 may be configured to output display data or information including the determined analyte level on the display unit 120 (FIG. 1) of the health monitor device with a medication dose calculation function 100.
  • the output unit 250 and the input section 240 may be integrated, for example, in the case where the display unit 120 is configured as a touch sensitive display where the patient may enter information or commands via the display area using, for example, a finger or stylus or any other suitable input device, and where, the touch sensitive display is configured as the user interface in an icon or motion driven environment, for example.
  • the communication interface 220 in one embodiment of the present disclosure includes a wireless communication section configured for bidirectional radio frequency (RF) communication with other devices to transmit and/or receive data to and from the health monitor device 100.
  • the communication interface 220 may also be configured to include physical ports or interfaces such as an USB port, an RS-232 port, or any other suitable electrical connection port to allow data communication between the health monitor device 100 and other external devices such as a computer terminal (for example, at a physician's office or in hospital environment), or other devices that are configured for similar complementary data communication.
  • the wireless communication section of the communication interface 220 may be configured for infrared communication, short-range communication (for example Bluetooth ® or ZigbeeTM), or any other suitable wireless communication mechanism to enable the health monitor device with a medication dose calculation function for communication with other devices such as infusion devices, analyte monitoring devices, computer terminals, communication enabled mobile telephones, personal digital assistants, or any other communication devices which the patient or user of the health monitor device 100 may use in conjunction therewith, in managing the treatment of a health condition, such as diabetes.
  • short-range communication for example Bluetooth ® or ZigbeeTM
  • any other suitable wireless communication mechanism to enable the health monitor device with a medication dose calculation function for communication with other devices such as infusion devices, analyte monitoring devices, computer terminals, communication enabled mobile telephones, personal digital assistants, or any other communication devices which the patient or user of the health monitor device 100 may use in conjunction therewith, in managing the treatment of a health condition, such as diabetes.
  • each of the user terminals 320 and the server terminal 310 are operatively coupled to the data network 330 via a corresponding data communication link 350.
  • the data network 330 via a corresponding data communication link 350.
  • the communication link 350 may include wired or wireless communication path which may be configured for secure, encrypted bi-directional data exchange over the data network 330.
  • the data communication link 350 in one embodiment may include Wi-Fi data communication, infrared data communication (for example Infrared Data Association (IrDA) communication), short-range communication (for example Bluetooth ® or
  • ZigbeeTM USB or Fire WireTM cable based data communication
  • Ethernet cable based data communication Ethernet cable based data communication
  • dial up modem data communication Ethernet cable based data communication
  • the user terminals 320 may include, among others, one of a personal computer (including a desk top or a laptop computer) or a handheld communication device such as an iPhone ® , Blackberry ® , internet access enabled mobile telephones, a bi-directional communication enabled pager, and a communication enabled personal digital assistant (PDA).
  • the user terminals 320 include an output unit such as a display and/or speakers, an input unit such as a keyboard or a touch- sensitive screen, as well as a controller such as a CPU for performing user instructed procedures at the user terminals 320.
  • the user terminals 320 may be configured to communicate with the data network 330 using a wireless data communication protocol such as Bluetooth ® , 801.1 lx, and ZigBeeTM. Additionally, the user terminal 320 may be also configured to
  • RF radio frequency
  • testing or monitoring device 340 may also be configured to connect to the respective user terminals 320 via a wired connection such as a USB connection, an RS-232 cable connection, an IEEE 1394 or Fire WireTM connection, or an Ethernet cable connection.
  • a wired connection such as a USB connection, an RS-232 cable connection, an IEEE 1394 or Fire WireTM connection, or an Ethernet cable connection.
  • the server terminal 310 in one embodiment may include a controller 311 operatively coupled to an input/output (I/O) interface unit 312, a read-only memory (ROM) 313, a random access memory (RAM) 314, and a storage unit 315.
  • the storage unit 315 includes a server application 316 and an operating system 317.
  • the controller 311 may in one embodiment be configured to communicate with the user terminals 320 over the data network 330 via the I/O interface unit 312, under the control of the various processes and routines stored in the ROM 313 and the storage unit 315 as well as user transmitted requests and information.
  • the server application 316 and the operating system 317 of the storage unit may be configured to provide a proprietary interface for the users, to execute secure and encrypted data communication over the data network 330.
  • the server terminal 310 may be configured to provide a proprietary internet-based user interface at a predetermined URL for the users to login from the user terminals 320, for example, for communication with the server terminal 310.
  • the data network 330 may include the internet, and wherein the server application 316 and the operating system 317 of the server terminal 310 are configured to provide a dedicated website for allowing the users to securely and easily login to their respective accounts using the user terminals 320 over the data network.
  • the storage unit 315 of the server terminal 310 in one embodiment may be configured to store data and information related to the user accounts such as, but not limited to, user contact information such as telephone and/or facsimile numbers, email address, health related monitoring data such as previously measured glucose levels, user specific basal profile information, bolus determination information, insulin sensitivity, trend information determined based on the measured glucose levels (and determined by the controller 311), and healthcare provider information for the user such as contact information for the user's physician, hospital, and nursing facilities.
  • user contact information such as telephone and/or facsimile numbers
  • email address email address
  • health related monitoring data such as previously measured glucose levels
  • user specific basal profile information such as previously measured glucose levels
  • bolus determination information such as insulin sensitivity
  • trend information determined based on the measured glucose levels and determined by the controller 3111
  • healthcare provider information for the user such as contact information for the user's physician, hospital, and nursing facilities.
  • the storage unit 315 may further be configured to store a glucose control chart, which is a transformation of glucose data obtained via the SMBG device described above or from a continuous glucose monitor such as the system and method described in US Patent Application Publication No.
  • the controller 311 of the server terminal 310 may be configured to determine trend
  • the server terminal 310 may be configured to generate and transmit to the user terminal 320 a color coded arrow indicator for display on the user terminal 320 to visually and easily inform the user of the projected or anticipated trend in the glucose level based on the measured glucose levels.
  • the controller 311 of the server terminal 310 may be configured to determine trend information based on insulin dosage information so as to determine and correspondingly generate for the user terminal 320 for display, a color coded indication of the user's projected future insulin dosage information, including projected increase or decrease in insulin dosage.
  • the controller 311 may be configured to alert the patient if the rate of change of the insulin dosage information over a period of time is above a certain threshold, possibly indicating an advancement in a user's health condition, such as a worsening of a diabetic condition.
  • a predetermined threshold it may be an indication that the user should visit their primary care physician in order to ascertain information relating to the health condition of the patient, and possibly determine a change in treatment or medication.
  • the server application 316 stored in the storage unit 315 of the server terminal 310 may be configured to perform, under the control of the controller 311, the various procedures and processes as discussed below in conjunction with FIGS. 4-10, as well as to store any information related to the user accounts and profiles within the scope of the present disclosure.
  • the present disclosure enables a physician to better evaluate the treatment of diabetic patients. This can be accomplished by
  • a patient's state of glucose control can be assessed in terms of 2 simple metrics.
  • the first relates to the ability to maintain a desirable glucose level on average.
  • the second relates to the ability to minimize the glucose excursion in the presence of meals and other factors.
  • median glucose is the first metric
  • the difference between the median and the 10 th percentile glucose is the second metric.
  • FIG. 4 depicts an example of a glucose control chart showing the states of 66 patients with diabetes.
  • the first metric is shown on the y-axis, and the second is shown on the x-axis.
  • a glucose testing schedule is needed to collect the data, for example from the SMBG system detailed above.
  • other clinically relevant information can be provided to enhance one's understanding of the impact on a planned treatment on the patient's various clinical states.
  • the present disclosure provides a method whereby the appropriate testing schedule is a function of an a priori (e.g., population-based) uncertainty of various testing schedules and the relevant clinical risk being reviewed for each patient, as a reflection of the patient's state of glucose control.
  • a priori e.g., population-based
  • a glucose control chart such as the one depicted in FIG. 4, can be used to provide a snapshot of a patient's glucose control.
  • the snapshot compresses multitudes of blood glucose (BG) data from self-monitoring BG (SMBG) fmgerstick readings and/or CGM system recordings over the course of days or weeks in between visits to the patient's healthcare provider (HCP), into a single point in the chart.
  • BG blood glucose
  • HCP patient's healthcare provider
  • FIG. 5 illustrates contour plots of a probability distribution function of a patient's state of glucose control. The distribution is obtained by simulating a patient performing a prescribed testing schedule over 14 days, and then using the bootstrap method to obtain more estimates (e.g., 400 additional estimates). This set of 400 glucose control chart data points is then used to create a probability distribution function. The 25%, 50%, and 75% contour heights are displayed in FIG. 5, which illustrates the uncertainty level of the patient's estimated glucose control level.
  • FIG. 6 shows the same distribution from the same patient, in the context of two different clinical risks, namely the risk of retinopathy (e.g., horizontal lines) and the risk of severe hypoglycemia (e.g., diagonal lines). If the difference between five hypoglycemic events per month and nine hypoglycemic events per month is clinically relevant, then given the patient's current state of glucose control, the prescribed testing schedule is marginally able to distinguish a clinically meaningful change in severe hypoglycemia risk.
  • the risk of retinopathy e.g., horizontal lines
  • severe hypoglycemia e.g., diagonal lines
  • the testing schedule is sufficient to distinguish a clinically meaningful change in risk of retinopathy. Since the contour line span is significantly smaller than the gap between the nearest two retinopathy risk lines, the testing schedule is adequate to measure the patient's level of retinopathy risk.
  • the testing schedule used for the illustration in FIG. 6 may or may not be adequate in assessing different clinical risks for the same patient. This was already shown by the width of the contour lines along the different clinical risk gradient lines. The risk of hypoglycemia and the risk of retinopathy are not the same.
  • the testing schedule used for the illustration of FIG. 6 may or may not be adequate in assessing a different set of clinical risks such as risk of nepropathy, risk of diabetic ketoacidosis, or risk of microvascular complications, the same patient with a significantly altered level of glucose control or the same clinical risks for a different patient.
  • the latter is illustrated in FIG. 7, where contour lines from 6 additional patients are plotted on the same chart. Note that as the patient's glucose variability increases, which is represented by a larger x-axis value, and as the patient's propensity for high BG value increases, which is represented by a larger y-axis value, the same testing schedule generates different uncertainty levels as indicated by the different contour size and shapes.
  • FIG. 8 illustrates the contour lines generated from a relatively stringent testing schedule on 7 patients. As compared with FIG. 7, the uncertainty levels are markedly decreased, particularly in areas of poor glucose control.
  • the more stringent testing schedule does not offer any better insight with respect to the two clinical risks discussed as examples.
  • patient F who achieves low glucose variability while
  • the more stringent testing schedule might help differentiate the patient's risk of retinopathy when compared to the next or previous visit (not shown in the figures).
  • the more stringent testing schedule clarifies patient D's risk of hypoglycemia.
  • the present disclosure contemplates a means to generate uncertainty levels of a patient's state of glucose control estimate as a function of specific SMBG testing schedules, a given clinical risk, and the two axes of the analyte control chart, such as the one illustrated in FIG. 9. Further, when different SMBG schedules are collected, for each schedule, uncertainty levels are computed a priori from the population data.
  • the population data can include CGM values, or SMBG values taken at a relatively frequent interval that approaches that of the CGM. For example, the SMBG values are taken once every 15 minutes or more frequently.
  • the population data can be obtained from human studies, wherein the participants wear a CGM system, or the data is collected from virtual participants in an "in-silico" manner, where the participants represent a wide range of demographics, state of diabetes, and various other physiological parameters.
  • the uncertainly levels from the population data can be projected along the gradient of a particular risk, which can result in a map of uncertainty levels for a given SMBG testing schedule and clinical risk as a function of the two axes of the glucose control chart, e.g., seen in FIG. 9.
  • FIG. 9 illustrates adequate and inadequate areas in the glucose control chart that can be identified for each combination of SMBG testing schedule and clinical risk being considered. An area is considered adequate if it has a sufficiently small uncertainty level relative to a clinically relevant gradient change. Otherwise, it would be considered inadequate. This is also illustrated in FIG. 8 for a given testing schedule and risk of retinopathy. Examples of testing schedules are seen now follow:
  • Schedule 1 4 times a day (i.e., 7am, 1 lam, 5pm, 10pm);
  • Schedule 3 7 times a day (i.e., Schedule 1 and tests at 9am, 1pm, and 7pm);
  • the shaded area seen in FIG. 9 corresponds to areas where the particular testing schedule is not adequate in detecting a clinically meaningful change in the risk of retinopathy, because the spread of the estimates along the direction of clinical change is much larger than the distance between local clinical risk lines.
  • a patient whose current state of glucose control puts their estimates in the inadequate space relative to the clinical risks being considered will need to follow a different testing schedule.
  • a patient whose glucose state is far from the boundary between inadequate and adequate region can use a potentially less demanding testing schedule.
  • Different clinical risks e.g., severe hypoglycemia, diabetic ketoacidosis, kidney failure, etc.
  • These testing schedules can be determined and suggested by the system described in FIGS. 1-3.
  • FIG. 10 is a flow diagram illustrating steps in a method for determining an analyte testing schedule based on a user's analyte control level and clinical risk. Execution of the method begins at process block 1002 wherein blood glucose data related to individuals in a population study is received by a system, such as the one described above.
  • the blood glucose data can be obtained, e.g., by a continuous glucose monitoring system or by self- monitoring blood glucose sampling by the individuals in the population study.
  • a bootstrap statistical method is applied to the information from each individual in the population study to determine a probability distribution (e.g., distribution function) of a glucose control chart value for each individual in the study.
  • the spread of the distribution function across the glucose control chart space can be quantified, as seen in process block 1006. More specifically, the spread can be a function of median and glucose variability.
  • process block 1008 one or more clinical risks are considered, such that for each combination of clinical risks being considered, areas in the glucose control chart are identified as adequate or inadequate for each blood glucose testing schedule.
  • the steps performed in process blocks 1002, 1004, 1006, and 1008 gather a priori population data 1000 and can be used to assess the blood glucose test schedule of an individual who was not part of the population study.
  • process block 1010 blood glucose data is collected from a patient not involved in the population data.
  • a single valued location of the patient's state of glucose control in the glucose control chart is determined in process block 1012.
  • the a priori population data gathered and processed in block 1000 is used to determine whether the patient's location in the glucose control chart, given the clinical risks considered and the patient's blood glucose testing schedule, identifies the patient's single valued state of glucose control as adequate or inadequate in process block 1014.
  • the determination performed in process block 1016 suggest an alternate blood glucose testing schedule that is most likely to be adequate around the patient's current state of glucose control and wait for new test data before making clinical decision. Otherwise, at process block 1016, proceed with a clinical decision and/or treatment, and provide a suggested blood glucose testing schedule for the next visit that is adequate for the expected change in the state of glucose control.
  • the present disclosure contemplates a means to utilize the mapped uncertainty levels in context of a specific set of clinical risks to provide a recommended SMBG testing schedule specific to each patient's latest state of glucose control. For example, the patient's initial data collected under a particular SMBG testing schedule is referenced against the map to see if the patient's glucose control estimate falls within the adequate or inadequate area. Then with the clinical risks being considered, a
  • SMBG testing schedule can be offered that will not render the patient's testing efforts meaningless for the clinical aspect being focused on.
  • an SMBG testing schedule that puts the next predicted state of glucose control into an adequate range can be considered, and one that is the least demanding can be offered to the patient.
  • the idea is to offer the least demanding schedule to the patient that will still allow a useful determination of changes in clinical risk while keeping in mind that while a more demanding schedule may be ideal, it can practically be the exact opposite when the patient does not appropriately follow the more demanding schedule.
  • a computer-implemented method for determining an analyte testing schedule based on a user's analyte control level and clinical risk may comprise receiving, at a data processing component, population data related to a population's analyte concentration, creating an analyte control chart based upon the received population data, determining one or more analyte testing schedules, determining a probability distribution function based upon the received population data, as a function of each analyte testing schedule and the location of each datapoint in the analyte control chart, determining for a plurality of clinical risks, areas where each analyte testing schedule is adequate or inadequate to distinguish each of the plurality of clinical risks, receiving data related to a user's analyte concentration, wherein the data related to the user's analyte concentration is acquired based on a particular analyte testing schedule, by at least one of self-monitoring analyte level sampling or utilizing
  • Certain aspects may include that the probability distribution function of the
  • the probability distribution function includes at least one uncertainty level of the population's analyte concentration in accordance to a particular analyte testing schedule.
  • Certain aspects may include that the probability distribution function is determined by estimating the analyte testing schedule of the population for a predetermined duration of time and then applying bootstrap statistics to the estimate.
  • analyte control chart includes a first metric correlated to a first axis of the analyte control chart and a second metric correlated to a second axis of the analyte control chart.
  • the at least one clinical risk includes at least one of a risk of retinopathy and a risk of severe hypoglycemia.
  • Certain aspects may include determining at least one gradient line of the analyte control chart, wherein the at least one gradient line is correlated to the at least one clinical risk.
  • Certain aspects may include that the population data is obtained from individuals wearing continuous analyte monitors.
  • Certain aspects may include that the individuals represent a variety of demographics, states of diabetes, and other physiological parameters. [0072] Certain aspects may include that at least one of the adequate areas and the inadequate areas are determined based upon the level of uncertainty to a clinically relevant gradient change.
  • Certain aspects may include that the different analyte testing schedule for the user is the least demanding schedule that still allows for a useful determination of changes in clinical risk.
  • Certain aspects may include quantifying a spread of distribution functions across the analyte control chart space for each analyte testing schedule.
  • Certain aspects may include that the location in the analyte control chart
  • corresponding to the user's state of analyte control is a single valued location of the user's state of analyte control in the analyte control chart.
  • an integrated analyte monitoring device assembly may comprise an analyte sensor for transcutaneous positioning through a skin layer and maintained in fluid contact with an interstitial fluid under the skin layer during a predetermined time period, the analyte sensor having a proximal portion and a distal portion, and sensor electronics coupled to the analyte sensor may comprise a circuit board having a conductive layer and a sensor antenna disposed on the conductive layer, one or more electrical contacts provided on the circuit board and coupled with the proximal portion of the analyte sensor to maintain continuous electrical communication, and a data processing component provided on the circuit board and in signal communication with the analyte sensor, wherein the data processing component is configured to execute one or more routines for processing signals received from the analyte sensor, to control the transmission of data associated with the processed signals received from the analyte sensor to a remote location using the sensor antenna in response to a request signal received from the remote location, to receive population data related to a population
  • Certain aspects may include that the probability distribution function of the
  • the probability distribution function includes at least one uncertainty level of the population's analyte concentration in accordance to a particular analyte testing schedule.
  • Certain aspects may include that the probability distribution function is determined by estimating the analyte testing schedule of the population for a predetermined duration of time and then applying bootstrap statistics to the estimate.
  • analyte control chart includes a first metric correlated to a first axis of the analyte control chart and a second metric correlated to a second axis of the analyte control chart.
  • the at least one clinical risk includes at least one of a risk of retinopathy and a risk of severe hypoglycemia.
  • Certain aspects may include that the data processing component is further
  • Certain aspects may include that the population data is obtained from individuals wearing continuous analyte monitors.
  • Certain aspects may include that the individuals represent a variety of demographics, states of diabetes, and other physiological parameters.
  • Certain aspects may include that at least one of the adequate areas and the
  • Certain aspects may include that the different analyte testing schedule of the user is the least demanding schedule that still allows for a useful determination of changes in clinical risk.
  • the data processing component can be configured to quantify a spread of distribution functions across the analyte control chart space for each analyte testing schedule.
  • the location in the analyte control chart corresponding to the user's state of analyte control is a single valued location of the user's state of analyte control in the analyte control chart.
  • an analyte monitoring device may comprise a data
  • analyte processing component provided on a circuit board and in signal communication with an analyte sensor; and a memory for storing instructions which, when executed by the data processing component, causes the data processing component to execute one or more routines for processing signals received from the analyte sensor, control the transmission of data associated with the processed signals received from the analyte sensor to a remote location using the sensor antenna in response to a request signal received from the remote location, receive population data related to a population's analyte concentration, create an analyte control chart based upon the received population data, determine one or more analyte testing schedules, determine a probability distribution function based upon the received population data, as a function of each analyte testing schedule and the location of each datapoint in the analyte control chart, determine for a plurality of clinical risks, areas where each analyte testing schedule is adequate or inadequate to distinguish each of the plurality of clinical risks, to receive data related to a user's analyte concentration from the
  • Certain aspects may include that the probability distribution function of the
  • the probability distribution function includes at least one uncertainty level of the population's analyte concentration in accordance to a particular analyte testing schedule.
  • the probability distribution function is determined by estimating the analyte testing schedule of the population for a predetermined duration of time and then applying bootstrap statistics to the estimate.
  • the analyte control chart includes a first metric correlated to a first axis of the analyte control chart and a second metric correlated to a second axis of the analyte control chart.
  • the at least one clinical risk includes at least one of a risk of retinopathy and a risk of severe hypoglycemia.
  • Certain aspects may include determining at least one gradient line of the analyte control chart, wherein the gradient line is correlated to the at least one clinical risk.
  • Certain aspects may include that the population data is obtained from individuals wearing continuous analyte monitors.
  • Certain aspects may include that the individuals represent a variety of demographics, states of diabetes, and other physiological parameters.
  • Certain aspects may include that at least one of the adequate areas and the
  • Certain aspects may include that the different analyte testing schedule for the user is the least demanding schedule that still allows for a useful determination of changes in clinical risk.
  • the data processing component can be configured to quantify a spread of distribution functions across the analyte control chart space for each analyte testing schedule.
  • Certain aspects may include that the location in the analyte control chart
  • corresponding to the user's state of analyte control is a single valued location of the user's state of analyte control in the analyte control chart.

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Abstract

L'invention concerne des procédés et des dispositifs permettant de déterminer un programme de dosage d'un analyte sur la base d'un taux témoin de l'analyte chez un utilisateur et d'un risque clinique. L'invention concerne également des systèmes et des kits.
PCT/US2012/062631 2011-10-31 2012-10-30 Détermination d'un programme de dosage du glucose Ceased WO2013074289A2 (fr)

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