Estimation of Future Insulin Sensitivity Distributions

• US 20190328966A1
• Filed: 04/16/2019
• Published: 10/31/2019
• Est. Priority Date: 04/26/2018
• Status: Active Grant

• Current Assignee: Tiro Medical Limited
• Original Assignee: Tiro Medical Limited
• Inventors: Chase, James Geoffrey, Knopp, Jennifer Launa, Pretty, Christopher Grant, Uyttendaele, Vincent
• Granted Patent: US 11,135,368 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61M 2205/50 with microprocessors or computers
• CPC Class Codes: A61M 2205/502 User interfaces, e.g. screens or keyboards
• CPC Class Codes: A61M 2205/52 with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2230/201 Glucose concentration
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A method and device for using insulin sensitivity values to estimate the distribution of future insulin sensitivity values for a given patient based on dimensional relationships between current, prior and delta insulin sensitivity values, and observed future insulin sensitivity values for similar patients or subjects, or based on an individual patient or subject'"'"'s historical values.

Claims

1. A method for controlling the blood glucose level of a patient comprising the steps of:

a) measuring a blood glucose level and determining the insulin sensitivity of the patient;

b) measuring, subsequent to step a), the blood glucose level and determining the current insulin sensitivity of the patient;

c) estimating with a multi-dimensional model, comprising delta SI, the adjustment needed to obtain a blood glucose level between a desired lower and upper confidence interval after administration of the estimated adjustment to the patient;

d) administering the estimated adjustment to the patient; and

e) repeating steps b) through d) until a desired state of health is achieved.

2. The method of claim 1, wherein the multi-dimensional model comprises clinical input data comprising current SI, prior SI, % delta SI, current blood glucose level and prior blood glucose level.

3. The method of claim 1, wherein the adjustment comprises insulin administration, nutrition, exercise, or combinations thereof.

4. The method of claim 1, wherein the desired lower and upper confidence interval comprises 5% and 95% or 10% and 90%, respectively.

5. The method of claim 2, wherein the clinical input data is from an individual or group or sub-cohort of individuals.

6. The method of claim 2, wherein the multi-dimensional model provides a risk assessment based upon a given insulin or nutrition input.

7. The method of claim 6, wherein the assessment is of light, mild, moderate or severe hypoglycemia defined by specific blood glucose levels.

8. The method of claim 2, wherein a likelihood is assessed of any set of input data occurring given the number of points grouped within the same set of input data and the total number of input data point groups in the entire set of associations.

9. The method of claim 2, wherein the multi-dimensional model is used to assess for potential input errors in SI, the clinical data used to evaluate SI, and/or other input data when an input has very low probability of occurrence and thus lies in a region of low or no existing data.

10. The method of claim 1, wherein the multi-dimensional model is based on the determined current insulin sensitivity and the % change in insulin sensitivity from a prior insulin sensitivity value, and the adjustment comprises the insulin/nutritional adjustment needed to obtain a blood glucose level between a desired lower and upper confidence interval after administration of the estimated insulin/nutritional adjustment to the patient.

11. A device for controlling the blood glucose levels of a patient, comprising:

a processor programmed to forecast with a multi-dimensional model based on the current insulin sensitivity and the % change in insulin sensitivity from a prior insulin sensitivity value, the adjustment needed to obtain a blood glucose level between a desired lower and upper confidence interval after administration of the estimated adjustment to the patient;

a memory;

a data input; and

a display.

12. The device of claim 11, wherein the processor is programmed to forecast with a multi-dimensional model based on the current insulin sensitivity and the % change in insulin sensitivity from a prior insulin sensitivity value, the insulin/nutritional adjustment needed to obtain a blood glucose level between the 5% and 95% confidence interval after administration of the estimated insulin/nutritional adjustment to a patient.