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EP-4740847-A2 - BOLUS CALCULATOR AND METHOD FOR CALCULATING A BOLUS

EP4740847A2EP 4740847 A2EP4740847 A2EP 4740847A2EP-4740847-A2

Abstract

The present disclosure relates to a bolus calculator (4.1) for determining a bolus (B) of insulin, the bolus calculator (4.1) having an input configured to be fed with a time series (h(t-τ)) of blood glucose values and to store at least one known pulse response (x(τ)) representing an active profile of at least one insulin, wherein the bolus calculator (4.1) is configured to convolute the time series (h(t-τ)) of blood glucose values with the known pulse response (x(τ)) to obtain the bolus (B) and to a method for calculating a bolus (B) of insulin, comprising feeding a time series (h(t-τ)) of blood glucose values into an input of a bolus calculator (4.1) and storing at least one known pulse response (x(τ)) representing an active profile of at least one insulin in the bolus calculator (4.1), convoluting the time series (h(t-τ)) of blood glucose values with the known pulse response (x(τ)) to obtain the bolus (B).

Inventors

  • KLEMM, THOMAS
  • SCHABBACH, MICHAEL

Assignees

  • Sanofi

Dates

Publication Date
20260513
Application Date
20181019

Claims (15)

  1. A bolus calculator (4.1) for determining a bolus (B) of insulin, the bolus calculator (4.1) having an input configured to be fed with a time series (h(t-τ)) of blood glucose values and to store at least one known pulse response (x(τ)) representing an active profile of at least one insulin, wherein the bolus calculator (4.1) is configured: - to take into account a dead time in the time series (h(t-τ)) due to blood glucose measurement of capillary blood of a human body (2), and - to convolute the time series (h(t-τ)) of blood glucose values with the known pulse response (x(τ)) to obtain the bolus (B).
  2. The bolus calculator (4.1) of claim 1, further configured to determine the dead time in the time series (h(t-τ)) by performing a teach-in run to measure a pulse response of the human body (2) on the basis of a blood glucose measurement of capillary blood of the human body as opposed to a measurement of a pulse response on the basis of a blood glucose measurement of venous blood of the human body.
  3. The bolus calculator (4.1) according to claim 1 or 2, configured to calculate a bolus (B) for one type (T) of insulin or for selecting one out of a plurality of types (T) of insulin and calculate the bolus (B) for this selected type (T).
  4. The bolus calculator (4.1) according to any one of the preceding claims, further configured to store and take into account patient specifics (PS), in particular at least one of age, sex, weight and body fat percentage.
  5. The bolus calculator (4.1) according to claim 4, further configured to store a characteristic map comprising a plurality of stored pulse responses (x(τ)) for one or more types (T) of insulin for different sets of patent specifics (PS).
  6. The bolus calculator (4.1) according to claim 4 or 5, further is configured to convolute all selectable types (T) of insulin for the set patient specifics (PS) with the time series (h(t-τ)) of measured blood glucose values and select the most appropriate type (T) of insulin.
  7. An arrangement (6) for determining a bolus (B) of insulin, comprising the bolus calculator (4.1) according to any one of the preceding claims, a human body model (9), a blood glucose sensor (3) adapted to perform a blood glucose measurement of venous blood or capillary blood of a human body (2) and a comparator (4.2) for comparing blood glucose values measured by the blood glucose sensor (3) with blood glucose values calculated by the human body model (9) before handing over to the bolus calculator (4.1).
  8. A method for calculating a bolus (B) of insulin, comprising: - feeding a time series (h(t-τ)) of blood glucose values into an input of a bolus calculator (4.1) and storing at least one known pulse response (x(τ)) representing an active profile of at least one insulin in the bolus calculator (4.1), - taking into account a dead time in the time series (h(t-τ)) due to blood glucose measurement of capillary blood of a human body (2), and - convoluting the time series (h(t-τ)) of blood glucose values with the known pulse response (x(τ)) to obtain the bolus (B).
  9. The method of claim 8, further comprising: determining the dead time in the time series (h(t-τ)) by performing a teach-in run thereby measuring a pulse response of the human body on the basis of a blood glucose measurement of capillary blood of the human body, wherein the dead time represents a delay of the pulse response as opposed to a measurement of a pulse response on the basis of a blood glucose measurement of venous blood of the human body.
  10. The method of claim 8 or 9, wherein a bolus (B) for one type (T) of insulin is calculated or wherein one out of a plurality of types (T) of insulin is selected and the bolus (B) for this selected type (T) is calculated.
  11. The method of any one of claims 8 to 10, comprising storing and taking into account patient specifics (PS), in particular at least one of age, sex, weight and body fat percentage.
  12. The method according to claim 11, further comprising storing a characteristic map comprising a plurality of stored pulse responses (x(τ)) for one or more types (T) of insulin for different sets of patient specifics (PS).
  13. The method according to claim 11 or 12, further comprising convoluting all selectable types (T) of insulin for the set patient specifics (PS) with the time series (h(t-τ)) of measured blood glucose values and selecting the most appropriate type (T) of insulin.
  14. The method according to any one of claims 9 to 13, further comprising performing a teach-in run to measure a pulse response of the human body (2) in order to determine the dead time.
  15. The method according to any one of the claims 8 to 13, further comprising storing measured blood glucose values and boluses (B) calculated to generate a history and taking the history into account when calculating boluses (B).

Description

Technical Field The disclosure generally relates to a bolus calculator and to a method for calculating a bolus. Background WO 2013/184896 A1 discloses that diabetes mellitus, often referred to as diabetes, is a chronic condition in which a person has elevated blood glucose levels that result from defects in the body's ability to produce and/or use insulin. There are three main types of diabetes. Type 1 diabetes usually strikes children and young adults, and may be autoimmune, genetic, and/or environmental. Type 2 diabetes accounts for 90-95% of diabetes cases and is linked to obesity and physical inactivity. Gestational diabetes is a form of glucose intolerance diagnosed during pregnancy and usually resolves spontaneously after delivery. Without treatment, diabetes can lead to severe complications such as heart disease, stroke, blindness, kidney failure, amputations, and death related to pneumonia and flu. Management of diabetes is complex as the level of blood glucose entering the bloodstream is dynamic. The variation of insulin that controls the transport of glucose out of the bloodstream also complicates diabetes management. Blood glucose levels are sensitive to diet and exercise, but also can be affected by sleep, stress, smoking, travel, illness, menses, and other psychological and lifestyle factors unique to individual patients. The dynamic nature of blood glucose and insulin, and all other factors affecting blood glucose, often require a person with diabetes to understand ongoing patterns and forecast blood glucose levels (or at least understand the actions that raise or lower glucose in the body). Therefore, therapy in the form of insulin or oral medications, or both, can be timed to maintain blood glucose levels in an appropriate range. Management of diabetes is often highly intrusive because of the need to consistently obtain reliable diagnostic information, follow prescribed therapy, and manage lifestyle on a daily basis. Daily diagnostic information, such as blood glucose, is typically obtained from a capillary blood sample with a lancing device and is then measured with a handheld blood glucose meter. Interstitial glucose levels may be obtained from a continuous glucose sensor worn on the body. Prescribed therapies may include insulin, oral medications, or both. Insulin can be delivered with a syringe, an insulin pen, an ambulatory infusion pump, or a combination of such devices. With insulin therapy, calculating the amount of insulin to be injected can require determining meal composition of carbohydrates, fat and proteins along with effects of exercise or other physiologic states. The management of lifestyle factors such as body weight, diet, and exercise can significantly influence the type and effectiveness of a therapy. Management of diabetes involves large amounts of diagnostic data and prescriptive data that are acquired from medical devices, personal healthcare devices, patient recorded information, healthcare professional biomarker data, prescribed medications and recorded information. Medical devices including self-monitoring bG meters, continuous glucose monitors, ambulatory insulin infusion pumps, diabetes analysis software, and diabetes device configuration software each of which generates and/or manages large amounts of diagnostic and prescriptive data. Personal healthcare devices include weight scales, pedometers and blood pressure cuffs. Patient recorded information includes information relating to meals, exercise and lifestyle as well as prescription and non-prescription medications. Healthcare professional biomarker data includes HbA1C, fasting glucose, cholesterol, triglycerides and glucose tolerance test results. Healthcare professional recorded information includes therapy and other information relating to the patient's treatment. There is a need for a patient device to aggregate, manipulate, manage, present, and communicate diagnostic data and prescriptive data from medical devices, personal healthcare devices, patient recorded information, biomarker information and recorded information in an efficient manner to improve the care and health of a person with diabetes, so the person with diabetes can lead a full life and reduce the risk of complications from diabetes. Additionally, there is a need for a diabetes management device that is able to provide an even more accurate bolus recommendation to the user based on various user inputs that take into account recent activities and events which may have an effect on the bG level of a patient to thus enhance the accuracy, convenience and/or efficiency of the device in generating a recommended bolus or a suggested carbohydrate amount for the user. There remains a need for an improved bolus calculator and an improved method for calculating a bolus. Summary An object of the present disclosure is to provide an improved bolus calculator and an improved method for calculating a bolus. The object is achieved by a bolus calculator accor