GB2443434A - Method for predicting nocturnal hypoglycaemia - Google Patents
Method for predicting nocturnal hypoglycaemia Download PDFInfo
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- GB2443434A GB2443434A GB0621822A GB0621822A GB2443434A GB 2443434 A GB2443434 A GB 2443434A GB 0621822 A GB0621822 A GB 0621822A GB 0621822 A GB0621822 A GB 0621822A GB 2443434 A GB2443434 A GB 2443434A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/66—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving blood sugars, e.g. galactose
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- G—PHYSICS
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/04—Endocrine or metabolic disorders
- G01N2800/042—Disorders of carbohydrate metabolism, e.g. diabetes, glucose metabolism
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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Abstract
A predictor of nocturnal hypoglycaemia for individuals with diabetes which provides a person with diabetes. The method selects blood glucose readings for the patient that may fall within a specified time or date range and associates a weight with the readings. Statistical methods are then applied to derive a risk metric. The patient may be alerted to take specific action based on the category of risk assigned. The device also provides indications of the blood glucose data supplied is not adequate for the analysis to proceed and produce a risk prediction, or a risk prediction with adequate confidence. These risk, action and alert indications are provided by the device as a message to the user. The message could be provided by any means the user can recognise including visual or audible means.
Description
PREDICTOR OF NOCTURNAL E-IYPOGLYCAEMIA FOR INDIVIDUALS WITH
DIABETES
The present invention relates to the prediction of hypoglycaernia for individuals with diabetes.
Individuals with diabetes have to carefully control the level of glucose in their blood.
Hypoglycaemia occurs when the blood glucose level in an individual with diabetes falls too low.
Some people with diabetes exhibit symptoms as their blood glucose level falls. Typical symptoms are sweating, dizziness, trembling, tingling hands/feet/lipS/tongUe, hunger, blurred vision, difficulty in concentrating, palpitations.
As blood glucose levels fall the individual will begin to exhibit changes in behaviour, largely due to insufficient glucose reaching the brain through the reduced distribution of glucose in the blood.
The consequences of untreated hypoglycaemia are fainting, and if intervention does not occur, coma and death.
Some people with diabetes may be able to correlate one or more of these symptoms with impending hypoglycaemia and are then able to take actions to prevent their blood glucose falling further and allow their blood glucose to rise, thereby preventing the episode of hypoglycaenhia.
Such people are, unfortunately in a small minority.
Many people with diabetes are not so fortunate. Either they do not exhibit symptoms as their blood glucose falls or they are not able to identify these symptoms as their cognitive ability is impaired by the low blood glucose level.
Such people have had diabetes for so long, and experienced hypoglycaemia so often, that they have become de-sensitised to the symptoms. This condition is called "Reduced Hypoglycaernic Awareness" and is of great concern to health care professionals caring for people with diabetes and the people with long term diabetes themselves.
The problem is further exacerbated if the person with diabetes is asleep. The sleeping person with diabetes may not be able to respond to symptoms (even if they can, and do, when awake). This is cause of great anxiety for people with diabetes, particularly those with long term diabetes and reduced hypoglycaemic awareness. They literally do not know if they are going to survive the night as they go to sleep.
This invention relates to a device which provides a person with diabetes with an indication of the risk of a nocturnal hypoglycaemic episode occurring, and indicates what action, based on advice provided by health care professionals, should be taken given this category of risk.
The device also provides indications if the blood glucose data supplied is not adequate for the analysis to proceed and produce a risk prediction, or a risk prediction with adequate confidence.
These risk, action and alert indications are provided by the device as a message to the user. The message could be provided by any means the user can recognise, for
example:
* Visual feedback using text on a display, which may or may not involve colour coding of the message to suit the indication; * Visual feedback using graphical images on a display; * Visual feedback illumination devices, such as light emitting diodes; * Audio feedback using synthesised voice messages or specific sounds to suit the indication.
The device uses the chronological log of data and time stamped blood glucose level readings already taken by the person with diabetes as part of their day to day care.
These readings are obtained using home blood glucose meters that are widely in use.
It is well known and documented that the blood glucose readings prior to the person with diabetes going to sleep are better indicators of the risk of nocturnal hypoglycaemia than readings taken, say, just after the person awakes from a nights sleep, or towards the beginning or middle of the persons day.
The device incorporates a date filter which allows a start date and end date to be specified. Only blood glucose readings with date stamps between these start and end dates are used by the device to predict the risk of nocturnal hypoglyCaelflia.
The device incorporates a time filter which allows the start and end time of a daily time period to be set. Only blood glucose readings with time stamps between these start and end times are used by the device to predict the risk of nocturnal hypoglycaemia. The start and end times of this period can be set to be: * from midnight to midnight so that the device uses all blood glucose readings in analysis and risk prediction; * between two specified times associated with the person with diabetes nominal bedtime and a few hours before this bedtime, so that the device only uses blood glucose readings during the pre-bedtime period in analysis and risk prediction.
The device is designed to accommodate filter start and end times that span midnight, for example a person who nominally goes to bed at 1 am and wishes to include blood glucose readings taken between 9pm and lam.
The device analyses a selection of these blood glucose readings and produces an indication of the risk of nocturnal hypoglycaemia.
Embodiments of the invention will now be described solely by way of an example and with reference to the accompanying drawings in which: Figure 1 shows an example of the indication provided by the device if too few blood glucose readings are available for analysis.
Figure 2 shows an example of the indication provided by the device if the user may be at little, or no risk, of nocturnal hypoglycaemia.
Figure 3 shows an example of the indication provided by the device if the user may be at some risk of nocturnal hypoglycaemia.
Figure 4 shows an example of the indication provided by the device if the user may be at significant risk of nocturnal hypoglycaemia.
Figure 5 shows an example of the indication provided by the device if the user may be at high risk of nocturnal hypoglycaemia.
Figure 6 shows an example of the indication provided by the device if the blood glucose data provided by the user is too compact.
Figure 7 shows an example of the indication provided by the device if the blood glucose data provided by the user is too sparse.
The device uses the chronological log of data and time stamped blood glucose level readings already taken by the person with diabetes as part of their day to day care.
There must be enough blood glucose readings found within the set date filter and time filter to perform the analysis with a required degree of confidence. The user of the device is informed, by a message if there are insufficient readings. An example device display for this situation is shown in Figure 1.
A risk metric value is produced by the analysis of the selected blood glucose reading values. The metric value is a weighted average of the selected blood glucose reading values. The weighting used places more emphasis of low blood glucose values and less and less emphasis on higher blood glucose readings. The risk metric value has a low value ror low risk and a high value for high risk.
As the degree of risk increases the value of the risk metric increases. The exact detail of weighting algorithm to be used is being evaluated in trials of the device.
Trials to date with one weighting algorithm have been successful. At the time of this application, further, more extensive trials are being planned.
A confidence interval for this risk metric value is also produced by the analysis.
The risk metric value and confidence intervals are inspected and categorised into one of several categories.
These categories represent a scale of risk of nocturnal hypoglyeamia occurring. This scale ranges from little risk, to a very high risk. The categorisation is accomplished by applying three thresholds to the metric and confidence interval or the risk metric value.
Relationship between the three thresholds and the category of risk are: * threshold 1 = boundary between little or no and some risk of nocturnal hypoglycaemia; * threshold 2 = boundary between some risk and significant risk of nocturnal hypoglycaemia; * threshold 3 = boundary between significant and high risk of nocturnal hypoglycaemia.
The process of categorising the risk metric value and confidence interval using these thresholds is illustrated
in Table 1.
Category Criteria Risk of Example of indication nocturnal presented to user by hypoglycaernia device _________ ________ Risk Action Colour _________ _____________ ______________ message message _______ 0 Risk metric Low Low None Green value Confidence Interval less than _________ threshold 1 _______________ _________ _________ ________ 1 Risk metric Some Some Maybe Blue value + Confidence Interval greater than (or equal to) threshold I and risk metric value less than _________ threshold 2 _______________ _________ _________ ________ 2 Risk metric Significant Yes YES Orange value greater than (or equal to) threshold 2 but less than _________ threshold 3 _______________ _________ _________ ________ 3 Risk metric High HIGH SEEK Red value HELP greater than (or equal to) _________ threshold 3 _______________ _________ _________ ________ Table 1 -Illustration of use of thresholds, categories, risk messages, action messages and colours The device displays the current category of risk based on the current selection of blood glucose readings. This display could include, but is not limited to, the value of the risk metric value and its' confidence interval, a textual description pertinent to the category and a textual message of recommended action. Example of a display for each category of risk are given in Figures 2, 3, 4 and 5.
The category boundaries, descriptions and messages are devised in collaboration with health care professionals.
The boundaries, descriptions and messages could be
tailored, or personalised, to the needs of a particular person with diabetes. Examples for each category are
given in table 1.
The person with diabetes can consult the device at anytime, particularly before going to sleep, and provided sufficient recent blood glucose readings are available, the device can inform the person with diabetes of their current category of nocturnal hypoglycaemic risk.
Depending on the current category of nocturnal hypoglycaemic risk the person with diabetes could go to bed with an increased degree of confidence if a low risk was predicted. If a high risk of nocturnal hypoglycaemia was predicted the person with diabetes could take action themselves, or seek advice from health care professionals in order to reduce the person with diabetes' risk of a nocturnal hypoglycaemic event.
People with diabetes may test their blood glucose more frequently when their blood glucose is low. This can lead to a series of blood glucose readings taken over a short period of time that reflect the same hypoglycaemiC event.
If all such readings are included in the determination of the risk metric value the contribution of this single hypoglycaemic event can be over inflated. The device incorporates a method to detect and use only the lowest blood glucose reading value in a series of values that have been taken in a short period of time. This method ensures only the true depth, i.e. lowest blood glucose reading value, is used to represent the hypoglycaemiC event.
To gain the best use of the device the person with diabetes needs to take sufficient blood glucose readings, within the specified time filter times, each day. The device provides feedback and advice on the data collection methods employed by the user. The device provides indications with regards to the temporal density of the blood glucose readings within the set filter time.
If there are, on average, too many blood glucose readings per day the data is too compact and an indication is given to the user, and example of such an indication is given in Figure 6.
If there are, on average, too few blood glucose readings per day, or there are days with no data, the data is too sparse and an indication is given to the user, and example of such an indication is given in Figure 7.
These indications of too compact or too sparse data inform the person with diabetes that they need to modify their blood glucose reading data collection methods to get the best risk predictions from the device.
The device could be implemented as a stand alone device into which the person with diabetes enters blood glucose reading dates, times and values. To avoid the need for double entry and possible transcription errors the device ideally would utilise that blood glucose data already captured by the home blood glucose meter of the person with diabetes.
The device could, but is not limited to, to be embodied in form of: a) A stand alone device into which the person with diabetes enters blood glucose reading dates, times and values; b)A new device that incorporates blood glucose meter technology; C) A device which is an evolution of the design of an existing blood glucose meter; d) A device which communicates with an existing blood glucose meter in order to transfer blood glucose data. A variety of platforms could be used as the basis for such a device. For instance, but not limited to: * A mobile phone; * A smart phone; * A Personal Digital Assistant (PDA); * A personal computer.
It will be apparent to one skilled in the art that various modifications and adaptations to the described system are possible within the scope of the invention.
The invention is not limited to particular equations used in the analysis method. As research continues, it is envisaged that improved mathematical representations may be incorporated into the invention.
Further modifications and improvements may be incorporated without departing from the scope of the invention herein intended.
Claims (10)
1. A method of modelling the risk of nocturnal hypoglycaemia for an individual, comprising the steps of: a) selecting several date and time stamped blood glucose readings from the available collection of blood glucose readings for the individual, the selection being based on one or more criteria, such as, but not limited to: i) the date stamp of the selected readings falling within a specified date range; ii) the time stamp of the selected readings falling within a specified time range.
b) analysing the selected date and time stamped blood glucose readings by applying a weight to each blood glucose reading value and then applying statistical techniques to the weighted blood glucose reading values to determine a risk metric value, and the confidence interval for this value, related to the risk of nocturnal hypoglycaemia.
2. A method as claimed for modelling the risk of nocturnal hypoglycaemia for an individual as claimed in Claim 1, wherein the determined risk metric value and confidence interval may be displayed to the user in a form relevant to the user, such as, but not limited to: a) a number representing the risk; b) a textual message representing the risk; C) a coloured visual indication representing the risk; d) an audible signal representing the risk.
3. A method as claimed for modelling the risk of nocturnal hypoglycaemia for an individual as claimed in Claims 1 to 2, whereby the determined risk metric value and confidence interval is assigned to a category of risk.
4. A method as claimed for modelling the risk of nocturnal hypoglycaemia for an individual as claimed in Claims 1 to 3, wherein the category of risk is displayed to the user in a form relevant to the user, such as, but not limited to: a) a number representing the risk category; b) a textual message representing the risk category; C) a coloured visual indication representing the risk category; d) an audible signal representing the risk category -
5. A method as claimed for modelling the risk of nocturnal hypoglycaemia for an individual as claimed in Claims 1 to 4, wherein prior to the determination of the risk metric value and confidence interval, the integrity of the selected date and time stamped blood glucose data is inspected, such inspection could, but is nbt limited to, identify and exclude from the risk calculation, blood glucose readings that form part of the same hypoglycaemic episode but are not the lowest blood glucose reading values for the particular episode.
6. A method as claimed for modelling the risk of nocturnal hypoglycaemia for an individual as claimed in Claims 1 to 5, wherein the exclusion of a selected blood glucose because it forms part a hypoglycaemiC episode already covered by other, lower, blood glucose readings may be displayed to the user in a form relevant to the user, such as, but not limited to: a) a textual message; b) a coloured visual indication; C) an audible signal.
7. A method as claimed for modelling the risk of nocturnal hypoglycaemia for an individual as claimed in Claims 1 to 6, wherein prior to the determination of the risk metric value and confidence interval, the integrity of the selected date and time stamped blood glucose data is inspected, such inspection would, but is not limited to: a) identify temporal gaps in the collection of the data and time stamped blood glucose data; b) identify that the collection of time stamped blood glucose data items is occurring too frequently; c) identify that the collection of time stamped blood glucose data items is not occurring frequently enough.
8. A method as claimed for modelling the risk of nocturnal hypoglycaemia for an individual as claimed in Claims 1 to 7, wherein the need to improve the users method of collection of the date and time stamped blood glucose data may be displayed to the user in a form relevant to the user, such as, but not limited to: a) a textual message offering advice on how to improve the method of data collection; b) a coloured visual indication offering advice on how to improve the method of data collection; C) an audible signal offering advice on how to improve the method of data collection.
9. A method as claimed in any one of the preceding claims, which is executed by a computer program.
10. A computer program adapted to execute the method claimed in Claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GB0621822A GB2443434A (en) | 2006-11-02 | 2006-11-02 | Method for predicting nocturnal hypoglycaemia |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GB0621822A GB2443434A (en) | 2006-11-02 | 2006-11-02 | Method for predicting nocturnal hypoglycaemia |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| GB0621822D0 GB0621822D0 (en) | 2006-12-13 |
| GB2443434A true GB2443434A (en) | 2008-05-07 |
Family
ID=37547191
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| GB0621822A Withdrawn GB2443434A (en) | 2006-11-02 | 2006-11-02 | Method for predicting nocturnal hypoglycaemia |
Country Status (1)
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| GB (1) | GB2443434A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7713229B2 (en) | 2003-11-06 | 2010-05-11 | Lifescan, Inc. | Drug delivery pen with event notification means |
| WO2010105743A1 (en) * | 2009-03-16 | 2010-09-23 | Roche Diagnostics Gmbh | Method for automatically generating a user-specific measurement data capturing regime for a discontinuous blood sugar measurement and data processing device and blood sugar measuring device |
| US8328719B2 (en) | 2004-12-29 | 2012-12-11 | Lifescan Scotland Limited | Method of inputting data into an analyte testing device |
| US8758245B2 (en) | 2007-03-20 | 2014-06-24 | Lifescan, Inc. | Systems and methods for pattern recognition in diabetes management |
| US8958991B2 (en) | 2008-08-15 | 2015-02-17 | Lifescan Scotland Limited | Analyte testing method and system |
| US8992475B2 (en) | 1998-08-18 | 2015-03-31 | Medtronic Minimed, Inc. | External infusion device with remote programming, bolus estimator and/or vibration alarm capabilities |
| US9351670B2 (en) | 2012-12-31 | 2016-05-31 | Abbott Diabetes Care Inc. | Glycemic risk determination based on variability of glucose levels |
| US10010291B2 (en) | 2013-03-15 | 2018-07-03 | Abbott Diabetes Care Inc. | System and method to manage diabetes based on glucose median, glucose variability, and hypoglycemic risk |
| US10383580B2 (en) | 2012-12-31 | 2019-08-20 | Abbott Diabetes Care Inc. | Analysis of glucose median, variability, and hypoglycemia risk for therapy guidance |
| CN113080949A (en) * | 2021-03-30 | 2021-07-09 | 北京京东拓先科技有限公司 | Hypoglycemia early warning method and device and computer-readable storage medium |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113951879B (en) * | 2021-12-21 | 2022-04-05 | 苏州百孝医疗科技有限公司 | Blood sugar prediction method and device, system for monitoring blood sugar level |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002015777A1 (en) * | 2000-08-18 | 2002-02-28 | Cygnus, Inc. | Methods and devices for prediction of hypoglycemic events |
| US6572542B1 (en) * | 2000-03-03 | 2003-06-03 | Medtronic, Inc. | System and method for monitoring and controlling the glycemic state of a patient |
-
2006
- 2006-11-02 GB GB0621822A patent/GB2443434A/en not_active Withdrawn
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6572542B1 (en) * | 2000-03-03 | 2003-06-03 | Medtronic, Inc. | System and method for monitoring and controlling the glycemic state of a patient |
| WO2002015777A1 (en) * | 2000-08-18 | 2002-02-28 | Cygnus, Inc. | Methods and devices for prediction of hypoglycemic events |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9415157B2 (en) | 1998-08-18 | 2016-08-16 | Medtronic Minimed, Inc. | External infusion device with remote programming, bolus estimator and/or vibration alarm capabilities |
| US10279110B2 (en) | 1998-08-18 | 2019-05-07 | Medtronic Minimed, Inc. | External infusion device with remote programming, bolus estimator and/or vibration alarm capabilities |
| US9744301B2 (en) | 1998-08-18 | 2017-08-29 | Medtronic Minimed, Inc. | External infusion device with remote programming, bolus estimator and/or vibration alarm capabilities |
| US8992475B2 (en) | 1998-08-18 | 2015-03-31 | Medtronic Minimed, Inc. | External infusion device with remote programming, bolus estimator and/or vibration alarm capabilities |
| US8333752B2 (en) | 2003-11-06 | 2012-12-18 | Lifescan, Inc. | Drug delivery with event notification |
| US8551039B2 (en) | 2003-11-06 | 2013-10-08 | Lifescan, Inc. | Drug delivery with event notification |
| US7713229B2 (en) | 2003-11-06 | 2010-05-11 | Lifescan, Inc. | Drug delivery pen with event notification means |
| US8328719B2 (en) | 2004-12-29 | 2012-12-11 | Lifescan Scotland Limited | Method of inputting data into an analyte testing device |
| US8348843B2 (en) | 2004-12-29 | 2013-01-08 | Lifescan Scotland Limited | Method of inputting data into an analyte testing device |
| US8758245B2 (en) | 2007-03-20 | 2014-06-24 | Lifescan, Inc. | Systems and methods for pattern recognition in diabetes management |
| US8958991B2 (en) | 2008-08-15 | 2015-02-17 | Lifescan Scotland Limited | Analyte testing method and system |
| WO2010105743A1 (en) * | 2009-03-16 | 2010-09-23 | Roche Diagnostics Gmbh | Method for automatically generating a user-specific measurement data capturing regime for a discontinuous blood sugar measurement and data processing device and blood sugar measuring device |
| US9703930B2 (en) | 2009-03-16 | 2017-07-11 | Roche Diabetes Care, Inc. | Automatic user-specific spot BG measuring routine based on user continuous BG measurements |
| US11331051B2 (en) | 2012-12-31 | 2022-05-17 | Abbott Diabetes Care Inc. | Analysis of glucose median, variability, and hypoglycemia risk for therapy guidance |
| US10019554B2 (en) | 2012-12-31 | 2018-07-10 | Abbott Diabetes Care Inc. | Glycemic risk determination based on variability of glucose |
| US10383580B2 (en) | 2012-12-31 | 2019-08-20 | Abbott Diabetes Care Inc. | Analysis of glucose median, variability, and hypoglycemia risk for therapy guidance |
| US9351670B2 (en) | 2012-12-31 | 2016-05-31 | Abbott Diabetes Care Inc. | Glycemic risk determination based on variability of glucose levels |
| US12178617B2 (en) | 2012-12-31 | 2024-12-31 | Abbott Diabetes Care Inc. | Analysis of glucose median, variability, and hypoglycemia risk for therapy guidance |
| US10010291B2 (en) | 2013-03-15 | 2018-07-03 | Abbott Diabetes Care Inc. | System and method to manage diabetes based on glucose median, glucose variability, and hypoglycemic risk |
| US11304664B2 (en) | 2013-03-15 | 2022-04-19 | Abbott Diabetes Care Inc. | System and method to manage diabetes based on glucose median, glucose variability, and hypoglycemic risk |
| US11963801B2 (en) | 2013-03-15 | 2024-04-23 | Abbott Diabetes Care Inc. | Systems and methods for use of insulin information for meal indication |
| USD1030780S1 (en) | 2013-03-15 | 2024-06-11 | Abbott Diabetes Care Inc. | Display screen or portion thereof with graphical user interface for continuous glucose monitoring |
| US12402841B2 (en) | 2013-03-15 | 2025-09-02 | Abbott Diabetes Care Inc. | Systems and methods for use of insulin information for meal indication |
| CN113080949A (en) * | 2021-03-30 | 2021-07-09 | 北京京东拓先科技有限公司 | Hypoglycemia early warning method and device and computer-readable storage medium |
| CN113080949B (en) * | 2021-03-30 | 2023-09-22 | 北京京东拓先科技有限公司 | Hypoglycemia early warning method and device and computer storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| GB0621822D0 (en) | 2006-12-13 |
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