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HK1182784A - Systems and methods for optimizing insulin dosage - Google Patents

Systems and methods for optimizing insulin dosage Download PDF

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Publication number
HK1182784A
HK1182784A HK13109902.3A HK13109902A HK1182784A HK 1182784 A HK1182784 A HK 1182784A HK 13109902 A HK13109902 A HK 13109902A HK 1182784 A HK1182784 A HK 1182784A
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HK
Hong Kong
Prior art keywords
biomarker
sampling
insulin
test method
data
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HK13109902.3A
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Chinese (zh)
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HK1182784B (en
Inventor
Steven Bousamra
Stefan Weinert
Juergen Rasch-Menges
Paul Douglas Walling
John F. Price
Heino Eikmeier
Birgit Kraeling
Karl Werner
Ulrich Porsch
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F. Hoffmann-La Roche Ag
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Application filed by F. Hoffmann-La Roche Ag filed Critical F. Hoffmann-La Roche Ag
Publication of HK1182784A publication Critical patent/HK1182784A/en
Publication of HK1182784B publication Critical patent/HK1182784B/en

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Description

System and method for optimizing insulin dosage
Cross Reference to Related Applications
This application is a continuation-in-part application of U.S. patent application serial No. 12/643,338 (WP 25378 US 1), filed 12/21/2009, which claims priority to U.S. provisional application serial No. 61/140,270, filed 23/12/2008, both of which are incorporated herein by reference in their entirety.
Technical Field
Embodiments of the present disclosure relate generally to diabetes management and, in particular, to methods and systems for optimizing insulin doses administered to diabetic patients.
Background
Diseases that last long or are frequently heavily attacked are generally defined as chronic diseases. Known chronic diseases include, inter alia, depression, obsessive-compulsive delusions, alcoholism, asthma, autoimmune diseases (e.g. ulcerative colitis, lupus erythematosus), osteoporosis, cancer and diabetes. Such chronic diseases require long-term care management in order to have effective long-term treatment. One of the functions of long-term care management after initial diagnosis is then to optimize the therapy of the patient's chronic disease.
In the case of diabetes, which is characterized by hyperglycemia due to inadequate insulin secretion, insulin function, or both, it is known that diabetes behaves differently in every human body due to the unique physiology of each individual interacting with different health and lifestyle factors, such as eating habits, weight, stress, illness, sleep, exercise, and medication.
Biomarkers are biologically derived indicators of a patient that indicate a biological or pathogenic process, pharmacological response, event (event), or condition (e.g., aging, disease or illness risk, presence or progression, etc.). For example, a biomarker may be an objective measure of a variable associated with a disease, which may be used as an indicator or predictor of the disease. In the case of diabetes, such biomarkers include values measured for glucose, lipids, triglycerides, and the like. A biomarker may also be a set of parameters from which the presence or risk of a disease can be inferred, rather than a measured value of the disease itself. When properly collected and assessed, biomarkers can provide useful information about medical issues with respect to a patient, and can be used as part of medical assessment, as medical control, and/or for medical optimization.
For Diabetes, clinicians typically treat diabetic patients according to published treatment guidelines, such as the Joslin Diabetes Center&Of Joslin ClinicClinical Guideline for Pharmacological Management of Type 2 Diabetes(2007) And Joslin Diabetes Center&Of Joslin ClinicClinical Guideline for Adults with Diabetes(2008). The guidelines may specify desired biomarker values, such as fasting blood glucose values of less than 100mg/dl, or a clinician may specify desired biomarker values based on the clinician's training and experience in treating a diabetic patient.
However, such guidelines do not specify biomarker collection protocols that are adjusted for parameters in order to support a particular therapy for optimizing therapy for diabetic patients. Subsequently, diabetics often must measure their glucose levels with little collection structure and little consideration of lifestyle factors. In particular, titration for basal insulin has identified a number of problems. The root of these problems is the lack of a centralized location for a particular dose of insulin that the patient is told to take during the titration optimization phase and during post-optimization (daily use). The problem may include the following: the patient only takes the number of labels attached to the package as originally prescribed by the physician; patients eat before sampling their blood glucose such that the sampling instance cannot be used or is not appropriate; the patient forgets to sample at the appropriate time; patients refuse to take more than the minimum number for fear; and no instruction from the physician is understood.
It is desirable to include a parameterized test method for implementing the best known basal rate titration algorithm in order to allow for the creation of a structured focused test protocol that assists the patient in insulin titration.
Disclosure of Invention
Against the above background, embodiments of the present test method are provided that are suitable for diabetic patients to optimize their insulin dosage. These embodiments for optimizing the titration of insulin, particularly basal insulin, help patients and physicians determine insulin levels that consistently result in fasting blood glucose values within a predetermined range and that do not have adverse events (e.g., hypoglycemic events or hyperglycemic events of varying severity) occurring at any time of the day. This test method embodiment provides a customized system and method for providing guidance and confidence (confidence) to a patient during titration of insulin. The test method benefits the physician by providing a monitoring embodiment of the physician's standardized practice, which yields higher confidence in the outcome of the titration. In addition, it is also expected that the test method will reduce the cost of optimization by reducing the number of visits to the clinic necessary to support the person performing the test method.
Embodiments of the disclosure may be implemented, for example, as follows: a paper tool; diabetes software integrated in a collection device such as a blood glucose meter; diabetes software integrated in a personal digital assistant, handheld computer, or mobile phone; diabetes software integrated in a device reader coupled to a computer; diabetes software operating on a computer such as a personal computer; and diabetes software accessed remotely over the internet.
In one embodiment, a test method is provided that is suitable for diabetic patients to optimize their insulin dosage. The method comprises the following steps: collecting one or more sampling sets of biomarker data, wherein each sampling set comprises a sufficient plurality of non-adverse sampling instances recorded over a collection period and each sampling instance comprises a biomarker reading at a single point in time, and wherein each sampling instance comprises an acceptable biomarker reading at a single point in time recorded at compliance with a compliance criterion; determining biomarker sampling parameters from each sampling set of biomarker data; comparing the biomarker sampling parameter to a target biomarker range; calculating an insulin adjustment parameter associated with the biomarker sampling parameter, wherein the insulin adjustment parameter equals zero when the biomarker sampling parameter falls within a target biomarker range, adjusting the insulin dosage by the amount of the insulin adjustment parameter if the biomarker sampling parameter does not fall within the target biomarker range, and exiting the test method if the adjusted insulin dosage is optimized, obtaining an optimized insulin dosage when the one or more biomarker sampling parameters fall within the target biomarker range. Other reasons for exiting the test method are described in detail below.
In another embodiment, a method for guiding a diabetic person through a test method for optimizing the administered dose of insulin is provided. The method utilizes a data processing system and includes instructing a diabetic patient, via a display unit, to collect one or more sampling sets of biomarker data, wherein each sampling set includes a sufficient plurality of non-adverse sampling instances recorded over a collection period, each sampling instance including a biomarker reading at a single point in time. The method also includes calculating a biomarker sampling parameter from each sampling set of biomarker data, calculating an insulin adjustment parameter associated with the biomarker sampling parameter if the biomarker sampling parameter falls outside the target biomarker range, and calculating an adjusted insulin dose from the present insulin dose and the insulin adjustment parameter if the biomarker sampling parameter falls outside the target biomarker range and if the insulin dose does not exceed the maximum dose. The method further includes informing the diabetic person to continue the test method with the adjusted insulin dosage or to exit the test method if an optimized insulin dosage is obtained, the optimized insulin dosage being obtained when one or more biomarker sampling parameters fall within the target biomarker range.
In another embodiment, a collection device configured to guide a diabetic person through a test method for optimizing an administered dose of insulin is provided. The apparatus comprises: a meter configured to measure one or more selected biomarkers; a processor disposed inside the meter and coupled to a memory, wherein the memory comprises a collection procedure; and software having instructions that, when executed by a processor, cause the processor to instruct the diabetic person to collect one or more sampling sets of biomarker data according to the collection protocol, wherein each sampling set comprises a sufficient plurality of non-adverse sampling instances recorded over a collection period, and each sampling instance comprises a biomarker reading at a single point in time. The software also includes instructions to; calculating a biomarker sampling parameter from each sampling set of biomarker data, calculating an insulin adjustment parameter associated with the biomarker sampling parameter if the biomarker sampling parameter falls outside the target biomarker range, and calculating an adjusted insulin dose from the present insulin dose and the insulin adjustment parameter if the biomarker sampling parameter falls outside the target biomarker range and if the insulin dose does not exceed the maximum dose. The software instructions also inform the diabetic person to continue the testing method with the adjusted insulin dosage. In another embodiment, the collection device may comprise a therapy device, such as an insulin pen.
These and other advantages and features of the invention disclosed herein will become more fully apparent from the following description, the accompanying drawings and the claims.
Drawings
The following detailed description of embodiments of the present invention can be best understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals.
FIG. 1 is a schematic diagram illustrating a long term care management system for diabetics and clinicians and others interested in long term care management of patients according to one embodiment of the present invention.
Fig. 2 and 2A are schematic diagrams illustrating an embodiment of a system suitable for implementing a structured testing method according to one embodiment of the present invention.
Fig. 3 shows a block diagram of an embodiment of the collecting device according to the invention.
FIG. 4 shows a depiction of one embodiment of a data record in tabular form generated on the collection device of FIG. 3 using a structured testing method in accordance with the present invention.
Fig. 5A-5D show a flow chart depicting a test method for optimizing titration of insulin in accordance with an embodiment of the present invention.
Detailed Description
The invention will be described with respect to various illustrative embodiments. Those skilled in the art will recognize that the present invention may be implemented in many different applications and embodiments and is not particularly limited in its application to the specific embodiments depicted herein. In particular, the invention will be discussed below in connection with diabetes management by sampling blood, but one of ordinary skill in the art will recognize that the invention can be modified for use with other types of fluids or analytes besides glucose and/or can be used to manage other chronic diseases besides diabetes.
As used herein with respect to the various illustrative embodiments described below, the following terms include (but are not limited to) the following meanings.
The term "biomarker" may mean a physiological variable measured to provide data related to a patient, such as a blood glucose value, a interstitial glucose value, an HbA1c value, a heart rate measurement, a blood pressure measurement, lipids, triglycerides, cholesterol, and the like.
The term "contextualizing" may mean documenting and interrelating conditions that already exist or are about to occur around the collection of a particular biomarker measurement. Preferably, data about documented and cross-correlated conditions that already exist or are about to occur around the collection of a particular biomarker may be stored together with and linked to the collected biomarker data. In particular, further evaluation of the collected biomarker data takes into account data on documenting and interrelating conditions, so that not only such data is assessed, but also the links between the data it is contextualized. The data about the documented and interrelated conditions may for example comprise information about the time, food and/or exercise that occurred around and/or simultaneously with the collection of specific biomarker measurements. For example, during a test protocol in a basal titration optimization set, the context of a structured collection protocol according to an embodiment of the present invention may be documented by utilizing entry criteria that verify a fasting state of a diabetic person prior to accepting biomarker values.
The term "contextualized biomarker data" may mean information about a cross-correlation condition in which specific biomarker measurements are collected in combination with measured values for the specific biomarker. In particular, the biomarker data is stored together with and linked to information about the cross-correlation condition in which the particular biomarker measurement was collected.
The term "biomarker sampling parameter" may mean a mathematical transformation of a requisite number of collected, e.g., non-adverse biomarker readings in a sampling set. The mathematical transformation may be, for example, averaging sample instances, summing sample instances, performing a graphical analysis on sample instances, performing a mathematical algorithm on a collection of samples, or a combination thereof.
The term "criteria" may mean one or more criteria, and may be at least one or more of guideline(s), rule(s), characteristic(s), and dimension(s) used to determine whether one or more conditions are met or met in order to begin, accept, and/or end one or more procedure steps, actions, and/or values.
The term "adherence" can mean that a person follows the structured collection procedure to properly perform the required procedure steps. For example, biomarker data should be measured under the prescribed conditions of the structured collection procedure. The adherence is then defined as appropriate if the prescribed conditions are given for the biomarker measurements. For example, the specified condition is a time-related condition and/or may illustratively include eating a meal, taking a fasting sample, eating a meal of a certain type within a desired time window, taking a fasting sample at a desired time, taking a minimum amount of sleep, and/or the like. The adherence can be defined as appropriate or inappropriate for a structured collection procedure, a set of sampling instances, or a single data point of the contextualized biomarker data. Preferably, the adherence can be defined as appropriate or inappropriate by a range of prescribed condition(s) or by selectively determined prescribed condition(s). Furthermore, the adherence can be calculated as an adherence ratio describing how much the adherence is given for a structured collection procedure or a single data point, in particular of contextualized biomarker data.
The term "adherence event" may mean that the person performing the structured collection procedure is not able to perform the procedure steps. For example, if a person does not collect data when required by the collection device, the adherence is determined to be inappropriate, resulting in an adherence event. In another example, the adherence criterion can be a first criterion for a patient fasting for 6 hours and a second criterion for collecting fasting bG values at a desired time. In this example, if the patient provided a bG sample at the requested time but fasting only 3 hours before the provision, the first adherence criterion was not met, although the second adherence criterion was met, and thus an adherence event to the first criterion would occur.
The term "violation event" is a form of an adherence event in which the person performing the structured collection (test) procedure (protocol) does not take the therapeutic at the recommended time, does not take the recommended amount, or both.
The term "adherence criterion" may include adherence and may mean the basis for comparing (e.g., evaluating) a value/information related to a measured value and/or a calculated value to a defined value/information or a defined range of values, wherein data may be accepted with a grant or positive reception based on the comparison. Adherence criteria may be applied to the contextualized biomarker data such that the biomarker data may be accepted based on a comparison of the contextualized data with respect to documentation and related conditions that exist or occur during collection of the particular biomarker. The adherence criteria may be similar to a sanity check for a given piece or group of information. Preferably, the adherence criterion can be applied to the data set or the information and rejected if the adherence criterion is not met. In particular, such rejected data will not be subsequently used for further calculations that provide therapy recommendations. The rejected data may primarily only be used to assess compliance and/or automatically trigger at least one other action. Such a triggered action may prompt the user to follow a structured collection procedure or a single required action, for example, so that the adherence criteria may be met.
The adherence criteria may also be applied to a single data point/information such that, for example, biomarker data may be accepted according to a comparison of contextualized data regarding documentation and related conditions that exist or occur during the collection of a particular biomarker. The adherence criterion may be interpreted as an "acceptance criterion" if it is applied to only a single data point.
Thus, the term "acceptance criteria" may include adherence criteria that are applied to a single data point, but may also include other criteria that may be applied to a single data point. A single data point/information may then be accepted depending on the contextualized data and, furthermore, depending on the measured condition and/or result of that particular biomarker. For example, if a measurement error is detected, the biomarker reading may be rejected because the acceptance criteria are not fulfilled, e.g., due to under-dose detection or other measurement errors, which may occur and may be detected by the system. Further, other criteria defining a particular range in which measurement data may be located may be defined as acceptance criteria for a single data point/information. The acceptance criteria may be applied to the contextualized biomarker data such that a single data point/information may be accepted as a function of contextualized data regarding documentation and related conditions that exist or occur during the collection of a particular biomarker and a comparison (e.g., evaluation) of such data to a defined value/information or defined range(s) of values for the contextualized data.
Furthermore, the acceptance criteria may comprise additional criteria related to measurement errors and/or a defined range of measurement values as described above. As used herein, a biomarker or event value may be "acceptable" if the user follows the appropriate and recommended steps (i.e., adherence), and in a preferred embodiment, the resulting data is within a predicted range. For example, before a sample is taken, an acceptance criterion may be established whether the steps leading to the taking of the sample are completed. For example, the processor displays a question in response to a request, "do you fast within the last 8 hours? "wherein a yes response received by the processor via the user interface satisfies the acceptance criteria for the step. In another example, after taking the samples, the processor may use other acceptance criterion(s) to evaluate the received data for rationality. For example, based on previous data, a fasting bG sample should be between 120 mg/dl-180 mg/dl, but the received value is 340mg/dl, and thus such acceptance criteria are not met because it is outside of the predefined range for acceptable values. In such an example, the processor may prompt for additional samples. If the resampling also fails (i.e., does not fall between 120 mg/dl-180 mg/dl), the assessment provided by the processor may be that the patient is not fasting, and thus the processor (as indicated by the acceptance criteria when the resampling fails) may automatically continue with events in the schedule of events accordingly. In this particular example, the acceptance criterion may be based on an adherence criterion for a single data point (to be fasting), which is the first acceptance criterion combined with a predefined range of blood glucose values that may be expected under the condition. The acceptance criterion may be met in total only if both criteria are fulfilled.
Further, the acceptance criteria for a single data point/information may be derived from criteria generated based on other data points/information. For example, a single data point cannot be accepted if the adherence criterion of the entire collection procedure or adjacent numerical adherence criteria, for which the single data point is measured during the entire collection procedure, is below a predefined threshold. In other words, the acceptance criteria for a single data point include not only the measured adherence criteria for a particular biomarker reading but also the adherence criteria for other biomarker readings or the entire collection procedure. Further, other criteria based on adjacent or related values of a particular single data point/information may be determined. For example, if pattern recognition is applied to biomarker readings having similar contextualized data (such as relating to a single data point/information), the single data point/information may not be acceptable given the reduced reliability based on the pattern recognition. For example, if a fasting blood glucose reading is detected as being too high for a particular person in a condition of contextualized data (as compared to a biomarker reading in a similar condition), it may be assumed that the data was recorded incorrectly, even if, for example, a measurement error and/or an adherence event cannot be detected by the system itself. Thus, the acceptance criterion may be defined by a predetermined criterion, e.g. by a predetermined numerical value, but may also be defined automatically based on data generated during the collection procedure, so that a specific criterion (in particular a numerical value) may be derived therefrom. Thus, the acceptance criterion may be used to prove the reliability of the individual data points/information, so that only those values that are important and/or have high reliability may be used for further calculations. Thus, the acceptance criterion may ensure that the calculation of the insulin adjustment parameter may be based only on these values reaching a predefined condition that is necessary for a correct insulin bolus calculation and that is accepted as a value with high reliability.
The term "data event requirement" may mean a query for data collection at a single point in space-time defined by a special set of cases, e.g., defined by events that are time-dependent or not time-dependent.
The term "decentralized disease state assessment" may mean a determination of the degree or extent of progression of a disease performed by utilizing biomarker measurements of interest, so as to deliver a numerical value without the need to send a sample to a laboratory for assessment.
The term "medical use case or problem" may mean at least one or more of a procedure, situation, condition, and/or problem that provides uncertainty as to the certainty of the existence of some medical fact, and is combined with a concept that has not yet been verified, but that if true will explain a particular fact or phenomenon. The medical use cases or questions may already be stored and stored in the system so that the diabetic person can choose between different medical use cases or questions. Alternatively, the medical use case or question may be defined by the diabetic person himself.
The terms "centralized," "structured," and "sporadic" are used interchangeably herein with the term "test," and may mean a predefined sequence in which a test is performed.
The terms "software" and "program" are used interchangeably herein.
FIG. 1 illustrates a long-term care management system 10 for a diabetic patient(s) 12 and a clinician(s) 14 and others 16 who are interested in the long-term care management of the patient 12. Patients 12 with writing difficulties may include those with metabolic syndrome, pre-diabetes, type 1 diabetes, type 2 diabetes, and gestational diabetes. Others 16 interested in the care of the patient may include family members, friends, support groups, and religious organizations, all of which may affect patient compliance with the therapy. The patient 12 may have access to a patient computer 18, such as a home computer, which may be connected to a public network 50 (wired or wireless), such as the internet, cellular network, etc., and coupled to the dongle, docking station, or device reader 22 for communication with an external portable device, such as a portable collection device 24. An example of a Device Reader is shown in the handbook "Accu-Chek. Smart Pix Device Reader digital person's Manual" (2008) available from Roche Diagnostics.
The collection device 24 may be essentially any portable electronic device that can be used as an acquisition mechanism to digitally determine and store biomarker value(s) according to a structured collection procedure, and which can be used to run the structured collection procedure and method of the present invention. More details regarding various illustrative embodiments of the structured collection procedure are provided in later sections below. In a preferred embodiment, the collection device 24 may be a self-monitoring blood glucose meter 26 or a continuous glucose monitor 28. One example of a Blood Glucose Meter is an Accu-Chek Active Meter and an Accu-Chek Aviva Meter described in the brochure "Accu-Chek Aviva Blood Glucose Meter Owner's Booklet (2007)", some portions of which are disclosed in U.S. Pat. No. 6,645,368B 1 entitled "method and method of using the Meter for determining the concentration of a component of a fluid", assigned to Roche Diagnostics operations, Inc., which is incorporated herein by reference. An example of a continuous glucose monitor is shown in U.S. Pat. No. 7,389,133 entitled "Method and device for connecting the monitoring of the concentration of an analyte" (2008, 6/17), assigned to Roche Diagnostics Operation, Inc., which is incorporated herein by reference.
In addition to the collection device 24, the patient 12 may use a variety of products to manage his or her diabetes, including: a test strip 30 carried in a vial 32 for use in the collection device 24; software 34 operable on the patient computer 18, the collection device 24, a handheld computing device 36 (such as a laptop computer, personal digital assistant, and/or mobile phone); and a paper tool 38. The software 34 may be preloaded or provided via computer-readable media 40 or provided via public network 50 and loaded for operation on patient computer 18, collection device 24, clinician computer/office workstation 25, and handheld computing device 36 as needed. In other embodiments, the software 34 may also be integrated into a device reader 22 coupled to a computer (e.g., computer 18 or 25) for operation thereon, or may be accessed remotely, e.g., from a server 52, over a public network 50.
Additional therapy devices 42 and other devices 44 may also be used by the patient 12 for a particular diabetes therapy. In addition, therapy device 42 may include devices such as an ambulatory infusion pump 46, an insulin pen 48, and a lancing device 51. An example of an ambulatory infusion Pump 46 includes, but is not limited to, Accu-Chek Spirit pumps described in the handbook "Accu-Chek Spirit insert Pump System Pump diabetes person Guide" (2007) available from Disetronic medical System AG. Other devices 44 may be medical devices that provide patient data such as blood pressure, health devices that provide patient data such as exercise information, and geriatric care devices that provide notifications to caregivers. The other devices 44 may be configured to communicate with each other according to the standards promulgated by Continua Health Alliance. These therapy devices may be separate or integrated into the collection devices and data processing devices described herein.
Clinicians 14 for diabetes are of a wide variety and may include, for example, nurses, nurse practitioners, physicians, endocrinologists, and other such health care providers. The clinician 14 typically has access to a clinician computer 25, such as a clinician office computer, which may also be provided with software 34. Also available on the computers 18, 25 by the patient 12 and clinician 14 are, for example, Microsoft ® health valultTMAnd GoogleTMHealth care record system 27, such as Health, to exchange information over public network 50 or over other network means (LAN, WAN, VPN, etc.) and store information, such as collected data from collection device 24, handheld collection device 36, blood glucose monitor 28, etc., into the patient's electronic medical record, such as EMR 53 (fig. 2A) that may be provided to computers 18, 25 and/or server 52 and from computers 18, 25 and/or server 52.
Most patients 12 and clinicians 14 can interact with each other and with others having computers/servers 52 through a public network 50. Such other persons may include the patient's employer 54, third party payers 56 (such as insurance companies that pay some or all of the patient's healthcare costs), pharmacies 58 that dispense certain diabetes consumables, hospitals 60, government agencies 62 (which may also be payers), and companies 64 that provide healthcare products and services for detecting, preventing, diagnosing, and treating diseases. The patient 12 may also permit others (such as an employer 54, a payer 54, a pharmacy 58, a hospital 60, and a government agency 62) to access the patient's electronic health record through a health care recording system 27, which health care recording system 27 may reside on the clinician computer 25 and/or one or more servers 52. Reference will also be made to fig. 2 hereinafter.
Fig. 2 illustrates one system embodiment suitable for implementing a structured testing method according to one embodiment of the present invention, which in another embodiment may be part of the chronic care management system 10, and communicates with such components via conventional wired or wireless communication means. The system 41 may include a clinician computer 25 in communication with the server 52 and the collection device 24. Communication between the clinician computer 25 and the server 52 may be facilitated by a communication link to the public network 50, to the private network 66, or to a combination of both. The private network 66 may be a local or wide area network (wired or wireless) connected to the public network 50 through network devices 68 such as (network) servers, routers, modems, hubs, and the like.
In one embodiment, the server 52 may be a central repository for a plurality of structured collection procedures (or protocols) 70a, 70b, 70c, 70d, with several details of exemplary structured collection procedures being provided in later sections. The server 52 and the network device 68 may also act as a data aggregator for several of the completed structured collection procedures 70a, 70b, 70c, 70 d. Accordingly, in such an embodiment, data from the collection device(s) of the patient 12 that has completed the collection procedure may then be provided from the server 52 and/or network device 68 to the clinician computer 25 when required in response to the acquisition of patient data.
In one embodiment, one or more of the plurality of structured collection procedures 70a, 70b, 70c, 70d on the server 52 may be provided over the public network 50, such as through a secure network interface 55 implemented on the patient computer 18, the clinician computer 25, and/or the collection device 24 (FIG. 2A, showing another embodiment of the system 41). In another embodiment, the clinician computer 25 may act as an interface (wired or wireless) 72 between the server 52 and the collection device 24. In another embodiment, the structured collection procedures 70a, 70b, 70c, 70d and software 34 may be provided on the computer readable medium 40 and loaded directly onto the patient computer 18, the clinician computer 25 and/or the collection device 24. In another embodiment, the structured collection procedure 70a, 70b, 70c, 70d may be provided pre-loaded (embedded) in the memory of the collection device 24. In other embodiments, the new/updated/modified structured collection procedures 70a, 70b, 70c, 70d may be sent between the patient computer 18, the clinician computer 25, the server 52, and/or the collection device 24 over the public network 50, the private network 66, over a direct device connection (wired or wireless) 74, or a combination thereof. Accordingly, in one embodiment, external devices, such as computers 18 and 25, may be used to establish communication links 72, 74 between the collection device 24 and other electronic devices, such as other remote Personal Computers (PCs) and/or servers, such as over the public network 50, such as the internet, and/or other communication networks, such as the private network 66 (e.g., LAN, WAN, VPN, etc.).
As a conventional personal computer/workstation, the clinician computer 25 may include a processor 76 that executes programs, such as the software 34, as well as programs, such as from memory 78 and/or the computer readable medium 40. The memory 78 may include system memory (RAM, ROM, EEPROM, etc.) and storage memory, such as a hard drive and/or flash memory (internal or external). The clinician computer 25 may also include a display driver 80 to interface a display 82 with the processor 76, an input/output connection 84 for connecting a diabetic person interface device 86, such as a keyboard and mouse (wired or wireless), and a computer readable drive 88 for portable memory and disk, such as the computer readable medium 40. The clinician computer 25 may also include a communication interface 90 for connecting to the public network 50 and other devices, such as the collection device 24 (wired or wireless), and a bus interface 92 for connecting the aforementioned electronic components to the processor 76. Reference will now be made to fig. 3 in the following.
Fig. 3 is a block diagram conceptually illustrating the portable collection device 24 depicted in fig. 2. In the illustrated embodiment, the collection device 24 may include one or more microprocessors, such as processor 102, which may be a central processing unit including at least one more single or multiple cores and a cache memory, which may be connected to a bus 104, which may include data, memory, control, and/or address buses. The collection device 24 may include software 34 that provides instruction code that causes the processor 102 of the device to implement the methods of the present invention, which will be discussed in later sections. The collection device 24 may include a display interface 106 that provides graphics, text, and other data from the bus 104 (or from a frame buffer not shown) for display on a display 108. The display interface 106 may be a display driver of an integrated graphics solution that utilizes a portion of the main memory 110 of the harvesting device 24, such as Random Access Memory (RAM), and processes from the processor 102, or may be a dedicated graphics processing unit. In another embodiment, the display interface 106 and the display 108 may additionally provide a touch screen interface to provide data to the collection device 24 in a well known manner.
The main memory 110 may be Random Access Memory (RAM) in one embodiment, and may include other memory such as ROM, PROM, EPROM or EEPROM, and combinations thereof, in other embodiments. In one embodiment, collection device 24 may include secondary memory 112, which may include, for example, a hard disk drive 114 and/or a computer-readable media drive 116 for computer-readable media 40, representing, for example, at least one of a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory connector (e.g., a USB connector, a firewire connector, a PC card slot), and so forth. The drive 116 reads from and/or writes to the computer-readable media 40 in a well-known manner. Computer-readable medium 40 represents a floppy disk, magnetic tape, compact disk (CD or DVD), flash drive, PC card, or the like, which is read by and written to by drive 116. It should be appreciated that the computer-readable medium 40 may have stored therein the software 34 and/or the structured collection procedures 70a, 70b, 70c, and 70d and data resulting from completed collections performed according to one or more of the collection procedures 70a, 70b, 70c, and 70 d.
In alternative embodiments, the secondary memory 112 may include other means for allowing the software 34, collection procedures 70a, 70b, 70c, 70d, other computer programs, or other instructions to be loaded into the collection device 24. Such means may include, for example, a removable storage unit 120 and an interface connector 122. Examples of such removable storage units/interfaces may include a program cartridge and cartridge interface, a removable memory chip (e.g., ROM, PROM, EPROM, EEPROM, etc.) and associated socket, and other removable storage units 120 (e.g., hard disk drive) and interface connector 122 that allow software and data to be transferred from the removable storage unit 120 to collection device 24.
In one embodiment, the collection device 24 may include a communication module 124. The communication module 124 allows software (e.g., software 34, collection procedures 70a, 70b, 70c, and 70 d) and data (e.g., data resulting from completed collections performed according to one or more of the collection procedures 70a, 70b, 70c, and 70 d) to be transferred between the collection device 24 and the external device(s) 126. Examples of communication module 124 may include one or more of the following: a modem, a network interface (such as an ethernet card), a communications port (e.g., USB, firewire, serial, parallel, etc.), a PC or PCMCIA slot and card, a wireless transceiver, and combinations thereof. The external device(s) 126 may be a patient computer 18, a clinician computer 25, a handheld computing device 36 such as a laptop computer, a Personal Digital Assistant (PDA), a mobile (cellular) phone, and/or a dongle, a docking station, or a device reader 22. In such an embodiment, the external device 126 may provide and/or connect to one or more of a modem, a network interface (such as an Ethernet card), a communications port (e.g., USB, firewire, serial, parallel, etc.), a PCMCIA slot and card, a wireless transceiver, and combinations thereof, to provide communications, such as with the clinician computer 25 or server 52, over the public network 50 or private network 66. Software and data transferred through the communication module 124 may be in the form of wired or wireless signals 128, which may be electronic, electromagnetic, optical, or other signals capable of being sent and received by the communication module 124. For example, it is known that signals 128 can be transmitted between communication module 124 and external device(s) 126 using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, an infrared link, other communication channels, and combinations thereof. Specific techniques for connecting electronic devices via wired and/or wireless connections (e.g., via USB and bluetooth, respectively) are known in the art.
In another embodiment, the collection device 24 may be used with an external device 132, such as provided as a handheld computer or mobile phone, to perform actions such as prompting the patient to take an action, collecting a data event, and performing a calculation on information. One example of a collection device provided as a handheld computer in combination with such an external device 126 is disclosed in U.S. patent application No. 11/424,757 entitled "System and method for collecting information from multiple floors therapy by means of a specified" filed on 16.6.2006, assigned to Roche Diagnostics Operations limited and incorporated herein by reference. Another example of a handheld computer is shown in the diabetes patient Guide entitled "Accu-Chek Pocket Assembly Software with Board calcium diabetes person Guide" (2007) available from Roche Diagnostics.
In the illustrated embodiment, the collection device 24 may provide a measurement engine 138 for reading a biosensor 140. The biosensor 140, which in one embodiment is a disposable test strip 30 (fig. 1), is used with the collection device 24 to receive a sample, e.g., capillary blood, which is exposed to an enzymatic reaction and measured by the measurement engine 138 by electrochemical techniques, optical techniques, or both, in order to measure and provide a biomarker value, e.g., blood glucose level. One example of a disposable test strip and measurement engine is disclosed in U.S. patent publication No. 2005/0016844 a1 "Reagent strips for test strips" (1/27/2005), which is assigned to Roche Diagnostics Operations limited and incorporated herein by reference. In other embodiments, the measurement engine 138 and biosensor 140 may be of a type used to provide biomarker values for other types of sampled fluids or analytes besides glucose, heart rate, blood pressure measurements, and combinations thereof. Such an alternative embodiment may be used for embodiments in which values from more than one biomarker type are required by the structured collection procedure according to the present invention. In another embodiment, biosensor 140 may be a sensor with indwelling catheter(s) or a subcutaneous tissue fluid sampling device(s), such as when collection device 24 is implemented as a Continuous Glucose Monitor (CGM) in communication with an infusion device, such as pump 46 (fig. 1). In other embodiments, collection device 24 may be a controller that implements software 34 and communicates between the infusion device (e.g., ambulatory infusion pump 46 and electronic insulin pen 48) and biosensor 140.
Data, including at least information collected by the biosensor 140, is provided to the processor 102 by the measurement engine 138, and the processor 102 may execute computer programs stored in the memory 110 to perform various calculations and processes using the data. By way Of example, such a Computer Program is described in U.S. patent application No. 12/492,667 entitled "Method, System, and Computer Program Product for Providing book Of Estimated True Mean Blood Glucose Value and Estimated Glucose (HbA1C) Value for Structured distances Of Blood Glucose", filed on 26.6.2009, assigned to Roche Diagnostics Operations, Inc. and incorporated herein by reference. The data from measurement engine 138 and the results of the calculations and processing performed by processor 102 using the data are referred to herein as self-monitored data. The self-monitoring data may include, but is not limited to, values of glucose values, insulin dosage values, insulin types, and parameters used by processor 102 to calculate future glucose values, supplemental insulin dosages, and carbohydrate supplement amounts for patient 12, as well as such values, dosages, and amounts. Such data is stored in the data file 145 of the memory 110 and/or 112 along with a date time stamp 169 for each measured glucose value and administered insulin dosage value. The internal clock 144 of the collection device 24 may provide the current date and time to the processor 102 for such use.
The collection device 24 may also provide a diabetic person interface 146, such as buttons, keys, trackballs, touch pads, touch screens, and the like, for data entry, program control and navigation of options, selections and data, making information requests, and the like. In one embodiment, the diabetic person interface 146 can include one or more buttons 147, 149 for providing input and navigation of data in the memories 110 and/or 112. In one embodiment, the diabetic person may use one or more buttons 147, 149 to enter (document) contextualized information, such as data related to the daily lifestyle of the patient 12, and confirm completion of prescribed tasks. Such lifestyle data can relate to food intake, drug use, energy levels, exercise, sleep, general health, and overall well-being of the patient 12 (e.g., happy, sad, calm, stressed, tired, etc.). Such lifestyle data may be recorded into the memory 110 and/or 112 of the collection device 24 as part of the self-monitored data by navigating through a menu of options displayed on the display 108 using the buttons 147, 149 and/or through a touch screen diabetes patient interface provided by the display 108. It should be appreciated that the diabetic person interface 146 may also be used to display self-monitored data, or a portion thereof, on the display 108, such as used by the processor 102 to display measured glucose levels as well as any entered data.
In one embodiment, the collection device 24 may be turned on by pressing any one or any combination of the buttons 147, 149. In another embodiment, the biosensor 140 is a test strip, and the collection device 24 may be automatically turned on when the test strip is inserted into the collection device 24 for measurement of the glucose level of a blood sample placed on the test strip by the measurement engine 138. In one embodiment, the collection device 24 may be turned off by holding down one of the buttons 147, 149 for a predefined period of time, or in another embodiment, the collection device 24 may be automatically turned off after not using the diabetic patient interface 146 for the predefined period of time.
An indicator 148 may also be connected to the processor 102 and may operate under the control of the processor 102 to issue audible, tactile (vibration), and/or visual alerts/reminders to the patient regarding the daily times of bG measurements and events, such as meals, possible future hypoglycemia, and the like. The collection device 24 is also provided with a suitable power source 150, as is known, to make the device portable with power.
As previously described, the collection device 24 may be pre-loaded with the software 34 or provided via the computer-readable medium 40 and receive the signal 128 directly via the communication module 124 or indirectly via the external device 132 and/or the network 50. When provided in the latter manner, the software 34 is stored in the main memory 110 (as shown) and/or the secondary memory 112 as it is received by the processor 102 of the collection device 24. The software 34 contains instructions that, when executed by the processor 102, enable the processor to perform the features/functions of the present invention, as discussed in later sections herein. In another embodiment, the software 34 may be stored in the computer-readable medium 40 and loaded into cache memory by the processor 102, thereby causing the processor 102 to perform the features/functions of the present invention as described herein. In another embodiment, the software 34 is implemented primarily in hardware logic, e.g., using hardware components such as Application Specific Integrated Circuits (ASICs). Implementing a hardware state machine to perform the various features/functions described herein will occur to those skilled in the relevant art(s). In another embodiment, the invention is implemented using a combination of both hardware and software.
In an example software embodiment of the present invention, the methods described below may be implemented using the C + + programming language, but may be implemented with other programs, such as (but not limited to) Visual Basic, C, C #, Java, or other programs available to those skilled in the art. In other embodiments, program 34 may be implemented using a scripting language or other proprietary interpretable language used in conjunction with an interpreter. Reference will also be made to fig. 4 below.
Fig. 4 graphically depicts a data file 145 containing data records 152 of self-monitoring data 154 resulting from a structured collection procedure according to one embodiment of the invention. The data records 152 (e.g., rows) along with the self-monitored data 154 (e.g., columns in some columns) may also provide context information 156 (e.g., other columns in some columns and by row and column header information) associated therewith. Such contextual information 156 may be collected automatically during the structured collection procedure, such as by input received automatically from any of the measurement engines, biosensors, and/or other devices, or may be collected by manual input received from the diabetic patient interface made by the patient in response to collection requirements (e.g., requirements displayed by the processor 102 on the display 108). Accordingly, since such contextual information 156 may be provided with each data record 152 in the preferred embodiment, such information may be readily available to the physician, and no further collection of such information is necessary after completion of the structured collection procedure, and thus need not be provided again by the patient, either manually or orally. In another embodiment, if such contextual information 156 and/or additional contextual information is collected after completion of a structured collection procedure according to the present invention, such information may be provided in the associated data file and/or record 145, 152 at a later time, such as by one of the computers 18, 25. Such information may then be associated with the self-monitoring data in the data file 145 and thus will not need to be provided again, either orally or manually. The processing in the latter embodiment may be required in the following cases: the structured collection procedure is implemented, or partially implemented, as a paper tool 38 for use with a collection device that is not capable of running the software 34 that implements the structured collection procedure.
It should be appreciated that the data file 145 (or a portion thereof, such as the self-monitored data 154 alone) may be transmitted/downloaded (wired or wireless) from the collection device 24 to another electronic device, such as the external device 132 (PC, PDA, or cell phone), or transmitted/downloaded to the clinician computer 25 via the network 50 via the communication module 124. The clinician may use the diabetes software provided on the clinician computer 25 to assess the received self-monitored data 154 of the patient 12 as well as the contextual information 156 to obtain a therapy result. One example of some of the functions that may be incorporated into Diabetes software and configured FOR a personal computer is Accu-Chek 360 Diabetes Management System available from Roche Diagnostics, which is disclosed in U.S. patent application No. 11/999,968 entitled "METHOD AND SYSTEM FOR SETTING TIME BLOCK", filed 12, 7, 2007 and assigned to Roche Diagnostics Operations, Inc. and incorporated herein by reference.
In a preferred embodiment, the collection device 24 may be provided as a portable blood glucose meter that is used by the patient 12 to record self-monitoring data including insulin dose readings and field measured glucose levels. Examples of such bG meters mentioned previously include, but are not limited to, both Accu-Chek Active meters and Accu-Chek Aviva systems, both of which are provided by Roche Diagnostics, Inc. and both of which are resistant to Accu-Chek 360 ® Tovae systems。The diabetes management Software is compatible for downloading test results to a personal computer, or is compatible with Accu-Chek pod Compass Software for downloading and communicating with the PDA. Accordingly, it should be appreciated that the collection device 24 may include the software and hardware necessary to process, analyze and interpret the self-monitored data and generate an appropriate data interpretation output according to a predefined flow sequence (as described in detail below). At one isIn an embodiment, the results of the data analysis and interpretation performed by the collection device 24 on the stored patient data may be displayed in the form of reports, trend monitoring graphs, and charts to assist the patient in managing their physiological condition and to support patient-physician communication. In other embodiments, the bG data from the collection device 24 can be used to generate reports (in hard copy or electronic form) by the external device 132 and/or the patient computer 18 and/or the clinician computer 25.
The collection device 24 may also provide the diabetic person and/or his or her clinician with at least one or more of the following things that may occur, including: a) editing data descriptions, such as titles and descriptions of records; b) storing the record at a specified location, particularly in a diabetes patient definable directory as previously described; c) retrieving the record for display; d) searching for records according to different criteria (date, time, title, description, etc.); e) categorizing the records according to different criteria (values of bG levels, dates, times, durations, titles, descriptions, etc.); f) deleting the record; g) exporting the record; and/or h) performing data comparisons, modifying records, and excluding records as is known.
Lifestyle, as used herein, may be generally described as a pattern of personal habits, such as dining, exercise, and work schedules. The individual may additionally be taking medication, such as insulin therapy or oral medication that they are required to take on a regular basis. The present invention implicitly takes into account the effect of this action on glucose.
It should be appreciated that the processor 102 of the collection device 24 may implement one or more structured collection procedures 70 provided in memory 110 and/or 112. In one embodiment, each structured collection procedure 70 may be stand-alone software, providing the necessary program instructions that, when executed by the processor 102, cause the processor to perform the structured collection procedure 70 as well as other prescribed functions. In other embodiments, each structured collection procedure 70 may be part of the software 34, and may then be selectively executed by the processor 102, in one embodiment by receiving a selection from the diabetes patient interface 146 from a menu list provided in the display 108, or in another embodiment by activation of a particular diabetes patient interface, such as a structured collection procedure run mode button (not shown) provided to the collection device 24. It should be appreciated that the software 34 likewise provides the necessary program instructions that, when executed by the processor 102, cause the processor to perform the structured collection procedure 70 as well as the other prescribed functions of the software 34 discussed herein. One suitable example of An alternative structured collection procedure With An alternative schema provided as a collection meter is disclosed in U.S. patent application No. 12/491,523 entitled "independent Blood Monitoring System With An Interactive Graphical person Interface And Methods of the proof," filed on 25.6.2009, which is assigned to Roche Diagnostics Operations, inc.
In another embodiment, command instructions may be sent from the clinician computer 25 and received by the processor 102 through the communication module 124 that place the collection device 24 in a collection mode that automatically runs the structured collection procedure 70. Such command instructions may specify which of the one or more structured collection procedures is to be run and/or provide the structured collection procedure to be run. In another embodiment, a list of defined medical use cases or medical questions may be presented by the processor 102 on the display 108, and a particular structured collection procedure 70 may be automatically selected by the processor 102 from among a plurality of structured collection procedures (e.g., procedures 70a, 70b, 70c, and 70 d) depending on the selection of a defined medical use case or medical question received by the processor 102 through the diabetic person interface 146.
In another embodiment, after selection, the structured collection procedure(s) 70 may be provided via a computer readable medium (e.g., 40) and loaded by collection device 24, downloaded from computer 18 or 25, other device(s) 132, or server 52. The server 52 may be, for example, a healthcare provider or company that provides such a predefined structured collection procedure 70 for download in accordance with selected defined medical use cases or issues. It should be appreciated that the structured collection procedure(s) 70 may be developed by a healthcare company (e.g., company 64) and implemented via a web page over the public network 50 and/or made available for download on the server 52, such as shown in fig. 2. In other embodiments, the notification that a new structured collection procedure 70 is available for the collection device 24 to help resolve a particular use case/medical issue that a diabetic person (e.g., a healthcare provider and a patient) may have may be provided in any standard manner, such as by postal letters/cards, email, text message, guest, and so forth.
In some embodiments, as previously mentioned, the paper tool 38 may perform some of the functions provided by the diabetes software 34. One example of some of the functionality that may be incorporated into the diabetes software 34 and configured as paper tools 38 is Accu-Chek 360 View Blood Glucose Analysis System (Accu-Chek 360 View Blood Glucose Analysis System) paper available from Roche Diagnostics, also disclosed in U.S. patent application No. 12/040,458 entitled "Device and method for assembling Blood Glucose control", filed on 29.2.2007, assigned to Roche Diagnostics Operations, Inc. and incorporated herein by reference.
In another embodiment, software 34 may be implemented on continuous glucose monitor 28 (FIG. 1). In this manner, continuous glucose monitor 28 may be used to obtain time resolved data. Such time resolved data can be used to identify fluctuations and trends that might not otherwise be noticed for on-site monitoring of blood glucose levels and standard HbA1c testing. For example, nighttime low glucose levels, high blood glucose levels between meals, early morning spikes in blood glucose levels, and how eating habits and physical activity can affect blood glucose and the effect of therapy changes.
In addition to the collection device 24 and software 34, the clinician 14 may prescribe other diabetes therapy devices for the patient 12, such as an ambulatory insulin pump 46 and an electronically-based insulin pen 48 (FIG. 1). The Insulin Pump 46 typically comprises Configuration Software such as disclosed in the handbook "Accu-Chek Instrument Pump Configuration Software" also available from Disetronic Medical Systems AG. The insulin pump 46, as well as the electronically-based insulin pen 48, can record and provide insulin dosage and other information to the computer, and thus can be used as another means of providing the biomarker data required by the structured collection procedure 70 (fig. 2) according to the present invention.
It should be recognized and as previously mentioned that one or more of the method steps discussed below may be configured as paper tool 38 (fig. 1), but preferably all of the method steps are carried out electronically on system 41 (fig. 2) or any electronic device/computer, such as collection device 24, having a processor and memory, and having program(s) resident in the memory. It is known that when a computer executes a program, the instruction codes of said program cause the processor of the computer to carry out the method steps associated therewith. In other embodiments, some or all of the method steps discussed below may be configured on a computer readable medium 40 storing instruction code for a program that, when executed by a computer, may cause the processor of the computer to perform the method steps associated therewith. These method steps will be discussed in more detail below with reference to fig. 5A.
Test method example for optimizing titration of insulin。
FIG. 5A provides an exemplary embodiment of a test method for optimizing insulin dose titration, which thereby produces an insulin dose that maintains biomarker levels within a desired range. In one embodiment, the titrated insulin may be basal insulin. After the test method is initiated, the insulin dose is typically an initially prescribed dose, for example an initially prescribed dose listed on a package. But other doses are also envisaged depending on what stage the test method is in, since the entry criteria can be considered before each biomarker reading. Thus, the initial dose may be an adjusted dose, a maximum allowable dose, or even an optimized dose, which is higher than the initial prescribed dose. It is envisaged that the test method may be used to obtain an optimised insulin value, or may be used after optimisation to verify that the insulin dose is still optimised.
In the embodiment of fig. 5A, the testing method may optionally require consideration of entry criteria 510 prior to beginning collection of biomarker data. It is contemplated that the diabetic person, the health care provider, or both may determine whether the entry criteria are met. The entry criteria may be established by a health care provider in some embodiments, and may be related to the age, weight, and medical history of the diabetic person. Thus, the testing method may require that the diabetic person be subjected to a check-up or physical examination in order to ensure that the diabetic person meets the entry criteria. For example, the entry criteria may specify Fasting Plasma Glucose (FPG) levels or glycated hemoglobin levels as determined by the HbA1c test. The normal range for the HbA1c test is between 4-6% for people without diabetes, so entry criteria may require values above about 6%, or in an exemplary embodiment between about 7.5% and about 10%. As an additional example of entry criteria, a fasting plasma glucose level of at least about 140mg/dl is required. The entry criteria may also set requirements regarding body weight or Body Mass Index (BMI). For example, the required BMI may be above about 25kg/m2, or between about 26kg/m2 and about 40kg/m 2. Further, the entry criteria may specify a desired age range (e.g., 30-70) or number of years (e.g., at least 2 years) with diabetes. Furthermore, although it is contemplated that the test method is applicable to persons with all types of diabetes, the entry criteria may limit the test method to type 2 diabetes. In addition, the entry criteria may focus on the current diabetes treatment regimen of the diabetic person. For example, the entry criteria may require that a treatment regimen for a diabetic be limited to oral anti-diabetic administration, i.e., no insulin injections. In addition, the entry criteria may require that the diabetic person be not ill or under stress. As previously mentioned, while the embodiment of fig. 5A is directed to consideration of entry criteria, the present testing method does not require consideration of entry criteria prior to collection of biomarker data. For example, referring to the additional embodiments of FIGS. 5B-D, the embodiment of FIG. 5B requires consideration of entry criteria; the embodiments of fig. 5C and 5D do not include such constraints.
Referring again to FIG. 5A, if the entry criteria are not met, then test method 515 will not be initiated. The diabetic person or health care provider may determine whether the entry criteria are met, or the data processor may determine whether the criteria are met. If the entry criteria are met 510, the diabetic person may begin the testing method. In some embodiments, however, the diabetic person may be required to meet the adherence criteria prior to collecting the biomarkers or taking insulin.
Adherence criteria are the requirements of the protocol a diabetic person must follow when performing the test method. In order to obtain a proper baseline for the biomarker readings, it may be beneficial to ensure that all readings are taken consistently, i.e., approximately at the same time of day for each sampling instance. Thus, the adherence criteria may specify that biomarker collection or insulin administration be performed at the same time each day. To assist the diabetic in meeting the adherence criteria, the data processor displays a prompt for the diabetic with an audio and/or visual reminder to collect a biomarker sample thereof and to enable the diabetic to set future reminders. In particular embodiments, the adherence criteria may also require that the diabetic person fasting for a set period of time before collecting the biomarker readings. The adherence criteria may also be directed to determining whether the diabetic person is ingesting the correct insulin dose. In additional embodiments, the adherence criteria may also require that there is no recent hypoglycemic event or severe hypoglycemic event (hypoglycemic event) within a set period of time (e.g., one week) before the biomarker data is collected. Furthermore, the adherence criteria may specify an exercise regimen or a feeding regimen for the diabetic person. As used herein, "eating regimen" means the typical eating regimen of a diabetic person in terms of calories, carbohydrate intake, and protein intake.
If the diabetic person is not able to meet any or all of the adherence criteria, the diabetic person may be notified that they are not able to meet the adherence criteria, for example, through a display of a blood glucose meter. If the diabetic person is not able to meet the adherence criteria, the data processor device may tag the adherence event, or the diabetic person may record the occurrence of the adherence event. After recording the adherence event, the testing method will typically continue. However, if too many adherence events are recorded (e.g., more than 4 in one sample period, more than 20 adherence events in the entire execution), the testing method may be terminated. In addition, the testing method may assess compliance events in different ways. For example, a hierarchical adherence event assessment may be performed in which adherence events are weighted. In one or more embodiments, if the adherence event has no effect on the biomarker data, its weight may not weigh as much as the adherence event affecting the biomarker data. For example, when a diabetic person has fasting for a necessary period of time before taking a fasting blood glucose reading but is unable to record that the reading is a fasting blood glucose reading, this will be classified as a less severe and thus less weighted adherence event for diabetes, since recording errors will not affect the fasting blood glucose reading. In contrast, fasting less than the requisite period of time will affect fasting glucose readings and therefore constitute a more serious and thus higher weighted adherence event.
If a violation event occurs (e.g., insulin administration is missed), the test method is more likely to be terminated than an adherence event (e.g., fasting less than the required fasting period) because the violation event affects the test method more severely. Since the present test method is directed to optimizing insulin administration, it is reasonable that an error in insulin dosage will be a serious violation event.
As with other instructions provided to the diabetic person throughout the testing method, the entry criteria or adherence criteria may be provided to the diabetic person in the form of paper instructions or by a data processing device as shown in FIG. 3 or a display unit on the processor 102. The data processing device may be any of the electronic devices described above. In one or more embodiments, the data processing device may be a computer or a blood glucose meter having a data processor and a memory unit therein. In addition to listing entry criteria, adherence criteria, or both, the data processing device may prompt the diabetic person to answer a medical question, wherein the answer to the medical question is used by the device to determine compliance with the entry criteria or adherence criteria. The data processing device may notify the diabetic person of their failure to comply with the entry criteria or the adherence criteria. For example, the data processing device may notify the diabetic person if the subsequent sampling instance is not taken near the same time as the first sampling instance. The patient may record sampling instances or answer medical questions by entering data events directly into the device or computer, where the processor 102 may store the information and provide additional analysis according to the parameters of the test method.
Referring again to fig. 5A, the diabetic person may begin collecting one or more data sample sets of biomarkers. Each sampling set comprises a sufficient number of non-adverse sampling instances recorded over a certain collection period, which means at least two sampling instances that are not indicative of an adverse event (e.g. a hypoglycemic or hyperglycemic event). Each sampling instance 540 includes biomarker readings at a single point in time. The collection time period for the sample set may be defined as a number of sample instances over a day, a number of sample instances over a week, a number of sample instances over consecutive weeks, or a number of sample instances over consecutive days of a week. The biomarkers may be associated with glucose levels, triglycerides, low density lipids, and high density lipids. In one exemplary embodiment, the biomarker reading is a blood glucose reading. In addition to the biomarker reading, each sampling instance may include the biomarker reading and other contextual data associated with the biomarker reading, wherein the contextual data is selected from the group consisting of: the time of collection, the date of collection, the time the most recent meal was consumed, confirmation that the requisite time period has been fasted, and combinations thereof. In the exemplary embodiment of fig. 5B, the test method occurs in a 7-day method that requires a diabetic patient to take insulin in the evening, followed by a fasting blood glucose reading collected the next morning. In addition to morning biomarker collection, a diabetic patient is also instructed to take additional biomarker readings when the diabetic patient encounters symptoms of hypoglycemia.
Referring again to fig. 5A, after the biomarker reading is collected, it is determined whether the biomarker reading indicates an adverse event 550. While the present discussion of adverse events focuses on hypoglycemic events and severe hypoglycemic events that may require medical assistance, it is contemplated that the adverse event may refer to undesirable levels of other biomarkers or medical indicators, such as lipid levels, blood pressure levels, and the like. In one embodiment, the determination regarding an adverse event may be performed by comparing the biomarker reading to a low threshold, such as the hypoglycemic event or severe hypoglycemic event threshold shown in Table 1 below. If the biomarker reading is below one or both of these thresholds, an adverse event may have occurred and should be recorded as an adverse event, or explicitly as a hypoglycemic event or severe hypoglycemic event. As previously mentioned, the determination may be performed by the data processor unit or may be manually entered by the diabetic person.
TABLE 1
Blood sugar range (mg/dl) Insulin regulating parameter (Unit)
Below 56 (severe hypoglycemic events) -2 to-4
56-72 (hypoglycemic events) 0
73 to 100 (target biomarker range) 0
100-119 +2
120-139 +4
140-179 +6
180 and more +8
If there is an adverse event (e.g., a severe hypoglycemic event), in one embodiment, the indication or data processing device may recommend that the diabetic person contact their healthcare provider. In another embodiment, the system may automatically contact a Health Care Provider (HCP). In addition, adverse events may optionally lead to dose reduction. Referring to Table 1 above, if it is a hypoglycemic event (between 56-72 mg/dl), the HCP 650 may be contacted, but the dosage is not adjusted (see FIG. 5). However, if it is a severe hypoglycemic event (below 56 mg/dl), the dose may be reduced by an amount (640), such as 2 units, 4 units, or another amount as indicated by a low biomarker reading. In particular embodiments, if the recorded adverse event is a second measured severe hypoglycemic event within the same day, the dose is not reduced. In other embodiments, the data processing device may utilize an algorithm to automatically reduce the insulin dosage and inform the diabetic person of the reduced insulin dosage. Further, the data processing device collecting the biomarker readings may automatically notify the healthcare provider of the adverse event, for example, by automated email and text message.
If the biomarker reading is not adverse, the next step depends on whether the sampling set 560 has a sufficient number of non-adverse sampling instances. If only one sampling instance is required for the sampling set, the biomarker sampling parameter may be calculated at that point; but as previously mentioned, the sample sets typically require multiple or at least two sample instances for each sample set. In an exemplary embodiment, two or more sample instances taken on consecutive days are required for each sample set. If multiple sampling instances are required, the diabetic person must continue to collect sampling instances.
Once the requisite number of sampling instances have been obtained for the sampling set, biomarker sampling parameters may be obtained 570. The biomarker sampling parameters may be determined by various algorithms or methods. For example, the biomarker sampling parameter may be determined by: averaging the sample instances, summing the sample instances, performing a graphical analysis on the sample instances, performing a mathematical algorithm on the sample set, or a combination thereof. In an exemplary embodiment, sampling instances (i.e., biomarker readings) are collected for at least three consecutive days, and the average of the three consecutive days is the biomarker sampling parameter.
After obtaining the biomarker sampling parameter, the value is compared to the target biomarker range. As used herein, a target biomarker range means an acceptable biomarker range in a diabetic person, which thereby indicates that insulin is producing the desired physiological response. If the biomarker sampling parameter falls outside the target biomarker range, an insulin adjustment parameter may be calculated 590. The insulin adjustment parameter is associated with the biomarker sampling parameter and is calculated from the biomarker sampling parameter. Various methods and algorithms are contemplated for calculating the insulin adjustment parameter. For example, the insulin adjustment parameter may be calculated by locating the insulin adjustment parameter associated with the biomarker parameter in an insulin adjustment parameter look-up table (see table 1 above). As previously shown in the exemplary insulin adjustment parameter look-up table of table 1, there may be multiple tiers indicating how many insulin doses should be adjusted. For example, fasting glucose levels below 100mg/dl but above 56mg/dl would not require adjustment of the insulin dose. The greater the deviation from the target range, the higher the modulation (in terms of units) of insulin.
After the insulin adjustment parameter is determined, the insulin dosage may be adjusted by the amount of the insulin adjustment parameter as long as the insulin adjustment does not increase the insulin dosage above the maximum allowable dosage. The adjusted insulin dose cannot exceed the maximum level set by the health care provider. After determining the adjusted insulin dose value, the diabetic person may then be instructed to collect at least one additional set of samples at the adjusted insulin dose according to the previously described collection procedure. The biomarker sampling parameter, the insulin adjustment parameter, and the adjusted insulin dosage may be calculated manually by the diabetic person or by a data processing device.
If the biomarker sampling parameter is within the target biomarker range, the insulin dosage is not adjusted. Furthermore, the insulin dosage may be considered optimized according to other applicable criteria. In particular, the insulin dose may be considered optimized if one biomarker sampling parameter is within the target biomarker range, or may be considered optimized 620 if at least two consecutive biomarker sampling parameters are within the target biomarker range. If the optimization definition requires at least two consecutive biomarker sampling parameters within the target biomarker range, the diabetic person is instructed to collect at least one additional set of samples at the adjusted insulin dose according to the previously described collection procedure. After the insulin dose is considered optimal, the diabetic is instructed to exit the test method. After exiting the test method 530, the diabetic person may conduct additional test methods to determine the future efficacy of the optimized dose.
In an alternative embodiment, if the diabetic has performed a test procedure for a long period of time, such as 6 months or more, the diabetic may be instructed to exit the test method 530. Further, as previously described, if there are multiple adherence or violation events, the test may be automatically terminated by the data processing device, or the diabetic may be instructed to exit the testing method.
Having described the invention in detail and by reference to specific embodiments thereof, it will be apparent that modifications and variations are possible without departing from the scope of the invention defined in the appended claims. More specifically, although some aspects of the present invention are identified herein as preferred or particularly advantageous, it is contemplated that the present invention is not necessarily limited to these preferred aspects of the invention.
All cited documents are incorporated herein by reference in relevant part; the citation of any document is not to be construed as an admission that it is prior art with respect to the present invention. To the extent that any meaning or definition of a term in this written document conflicts with any meaning or definition of the term in a document incorporated by reference, the meaning or definition assigned to them in this written document shall govern.
While particular embodiments of the present invention have been illustrated and described, it would be obvious to those skilled in the art that various other changes and modifications can be made without departing from the spirit and scope of the invention. It is therefore intended to cover in the appended claims all such changes and modifications that are within the scope of this invention.

Claims (37)

1. A collection device configured to guide a diabetic person through a test method for optimizing dosing, comprising:
a meter configured to measure one or more selected biomarkers;
a processor coupled to a memory, wherein the memory comprises a collection procedure; and
software having instructions that, when executed by the processor, cause the processor to:
instructing the diabetic person, via a user interface, to collect one or more sampling sets of biomarker data according to the collection procedure, wherein each sampling set comprises a sufficient number of sampling instances recorded over a collection period, and each sampling instance comprises an acceptable biomarker reading recorded at a compliance acceptance criterion; and
a biomarker sampling parameter is calculated from each sampling set of biomarker data,
if the biomarker sampling parameter falls outside a target biomarker range, calculating an insulin adjustment parameter associated with the biomarker sampling parameter, an
Calculating an adjusted insulin dose based on the present insulin dose and the insulin adjustment parameter if the biomarker sampling parameter falls outside the target biomarker range and if the insulin dose does not exceed the maximum dose; and
the diabetic is notified to continue the testing method with the adjusted insulin dosage.
2. The collection system of claim 1, wherein the software causes the processor to calculate the biomarker sampling parameters considering only biomarker data collected in compliance with the acceptance criteria.
3. The collection system of claim 1, wherein the collection device is a continuous glucose monitor for obtaining time resolved glucose information provided to the processor as biomarker data.
4. The collection system of claim 1, further comprising a scalpel operative to pierce the skin of the diabetic patient to obtain a blood glucose biomarker.
5. The collection system of claim 1, further comprising a therapy device configured to cause a diabetic person to take insulin.
6. The collection system of claim 5, wherein the therapy device is an insulin pen.
7. A method for guiding a diabetic person through a test method for optimizing the administered dose of insulin, the method utilizing a data processing system and comprising:
instructing the diabetic person via the display unit to collect one or more sampling sets of biomarker data, wherein each sampling set comprises a sufficient number of sampling instances recorded in a memory over a collection period, each sampling instance comprising an acceptable biomarker reading recorded in the memory, whereby the processor determines compliance with the stored acceptance criteria;
by the processor or one or more other processors:
a biomarker sampling parameter is calculated from each sampling set of biomarker data,
if the biomarker sampling parameter falls outside a target biomarker range, calculating an insulin adjustment parameter associated with the biomarker sampling parameter, an
Calculating an adjusted insulin dose based on the present insulin dose and the insulin adjustment parameter if the biomarker sampling parameter falls outside the target biomarker range and if the insulin dose does not exceed the maximum dose; and
the diabetic person is notified via the data interface to continue the test method with the adjusted insulin dose or to exit the test method if an optimized insulin dose is obtained, which is obtained when one or more biomarker sampling parameters fall within a target biomarker range.
8. The method of claim 7, wherein only the biomarker data considered for calculating the biomarker sampling parameter are those that comply with the acceptance criteria.
9. The method of claim 7, further comprising prompting the diabetic person to answer a medical question, wherein the answer to the medical question is used to determine compliance with entry criteria or compliance with criteria.
10. The method of claim 7, further comprising automatically notifying the health care provider of the adverse event.
11. The method of claim 7, further comprising automatically reducing an insulin dose and indicating the reduced insulin dose to the diabetic person.
12. The method of claim 7, further comprising notifying the diabetic person if the subsequent sampling instance was not taken around the same time as the initial sampling instance.
13. A test method suitable for diabetic patients to optimize their insulin dosage, comprising:
collecting one or more sampling sets of biomarker data, wherein each sampling set comprises a sufficient number of sampling instances recorded over a collection period and each sampling instance comprises an acceptable biomarker reading recorded at a compliance acceptance criterion;
determining biomarker sampling parameters from a sampling set of such biomarker data whereby only those compliance with said acceptance criteria are considered;
comparing the biomarker sampling parameter to a target biomarker range;
calculating an insulin adjustment parameter associated with the biomarker sampling parameter if the biomarker sampling parameter falls outside the target biomarker range;
adjusting the insulin dose by the amount of the insulin adjustment parameter if the biomarker sampling parameter falls outside the target biomarker range and if the insulin dose does not exceed a maximum dose; and
exiting the test method if the adjusted insulin dose is optimized, obtaining an optimized insulin dose when one or more biomarker sampling parameters fall within the target biomarker range.
14. The test method according to claim 13, wherein the insulin dosage is optimized when at least two consecutive biomarker sampling parameters fall within a target biomarker range.
15. The test method of claim 13, further comprising performing a new test method after obtaining the optimized dose.
16. The test method of claim 13, wherein the collection period of the sample set is defined as: multiple sampling instances over a day, multiple sampling instances over a week, multiple sampling instances over consecutive weeks, or multiple sampling instances over consecutive days over a week.
17. The test method of claim 13, wherein each sampling instance comprises a biomarker reading and other contextual data associated with the biomarker reading, wherein the contextual data is selected from the group consisting of: a collection time, a collection date, a time when the most recent meal was consumed, and combinations thereof.
18. The test method of claim 13, further comprising collecting one or more additional sample sets of biomarker data when the biomarker sampling parameter falls outside the target biomarker range.
19. The test method of claim 13, further comprising notifying the health care provider of any biomarker readings indicative of an adverse event.
20. The test method of claim 19, further comprising reducing the insulin dosage for any biomarker reading indicative of an adverse event.
21. The test method of claim 19, wherein the adverse event is a hypoglycemic event.
22. The test method of claim 13, wherein biomarker readings below a lower threshold are indicative of an adverse event.
23. The test method of claim 13, further comprising contacting a health care provider for any biomarker readings indicative of an adverse event.
24. The test method of claim 13 wherein the insulin dosage is reduced for any biomarker reading indicative of an adverse event.
25. The test method of claim 13, further comprising meeting an entry criterion required to begin collecting the one or more sample sets.
26. The test method of claim 13, wherein the adherence criteria requires that a prescribed dose be taken throughout the collection period.
27. The test method of claim 13, wherein the adherence criterion requires a fasting period prior to collecting the sampled set of biomarker data.
28. The test method of claim 25, wherein said entry criteria requires said diabetic person to have type 2 diabetes.
29. The test method according to claim 25, wherein said entry criteria requires that said diabetic person be limited to oral diabetes medication prior to performing said test method.
30. The test method of claim 13, wherein the maximum insulin dose is set by a health care provider.
31. The test method of claim 13, wherein the biomarker sampling parameters are determined by averaging sampling instances, summing sampling instances, performing a graphical analysis on sampling instances, performing a mathematical algorithm on a collection of samples, or a combination thereof.
32. The test method of claim 13, wherein the biomarker sampling parameter is determined by averaging sampling instances.
33. The test method of claim 13, wherein the calculation of the insulin adjustment parameter comprises: positioning the insulin adjustment parameter associated with the biomarker parameter in an insulin adjustment parameter look-up table, utilizing an algorithm, and combinations thereof.
34. The test method of claim 13, wherein the biomarker reading comprises information about the type of biomarker selected from glucose, triglycerides, low density lipids, and high density lipids.
35. The test method of claim 13, wherein the biomarker reading is a blood glucose reading.
36. The test method of claim 13, wherein the insulin is basal insulin.
37. The test method of claim 13, further comprising exiting the test method upon the occurrence of a violation event.
HK13109902.3A 2010-06-18 2011-06-11 Systems and methods for optimizing insulin dosage HK1182784B (en)

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Application Number Priority Date Filing Date Title
US12/818,310 2010-06-18

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HK1182784A true HK1182784A (en) 2013-12-06
HK1182784B HK1182784B (en) 2019-05-24

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