Method, system and computer readable medium for assessing actionable glycemic risk

• US 10,638,981 B2
• Filed: 02/16/2016
• Issued: 05/05/2020
• Est. Priority Date: 02/16/2015
• Status: Active Grant

• Current Assignee: University of Virginia
• Original Assignee: University of Virginia Patent Foundation (University of Virginia)
• Inventors: Patek, Stephen D
• Primary Examiner(s): Wilson, Kaylee R
• Assistant Examiner(s): Shah, Jay B
• Application Number:
  US15/551,503
• Publication Number:
  US 20180020988A1
• Time in Patent Office: 1,540 Days
• Field of Search: None
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• 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 2205/3584 using modem, internet or bluetooth
• 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: G06F 17/12 Simultaneous equations , e.g. systems of linear equations
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A system, method and non-transient computer readable medium for assessing the opportunity to address either hyperglycemic or hypoglycemic risk (or both) in patients with diabetes based on historical continuous glucose monitoring (CGM) data.

Claims

1. A method for assessing actionable glycemic risk comprising:

obtaining an output continuous glucose monitoring (BG) signal at a time t;

determining a glycemic cost signal at the time t based on the output continuous glucose monitoring BGsignal with a glycemic cost function algorithm that attributes equal cost to each of two end points (BGtarget,lo, BGtarget,hi) of a desired blood glucose target range;

using the glycemic cost signal to quantify actionable risk and unaddressable risk by determining an instantaneous hypoglycemia risk at the time t and an instantaneous hyperglycemia risk at the time t based on the glycemic cost signal, wherein actionable risk includes actionable hypoglycemia risk and actionable hyperglycemia risk;

averaging each of the instantaneous hypoglycemia risk and the instantaneous hyperglycemia risk over a defined interval to determine a moving average hypoglycemia risk and a moving average hyperglycemia risk;

averaging each of the moving average hypoglycemia risk and the moving average hyperglycemia risk across multiple days to generate a hypoglycemia risk profile (lop(s)) and a hyperglycemia risk profile (hip(s)) at time s of the day;

administering insulin to a patient, via an insulin device, based on the generated hypoglycemia risk profile (lop(s)) and hyperglycemia risk profile (hip(s)).

2. The method of claim 1, wherein the glycemic cost function algorithm is:

3. The method of claim 1, wherein the instantaneous hypoglycemia risk and the instantaneous hyperglycemia risk are determined by the algorithm:

rlo(BG)=J(BG), if BG<

sqrt(BGtarget,lo*BGtarget,hi) 0, otherwise

rhi(BG)=J(BG), if BG>

=sqrt(BGtarget,lo*BGtarget,hi) 0, otherwise, wherein

J(BG) is the glycemic cost function, rlo(BG) is the instantaneous hypoglycemia risk and rhi(BG) is the instantaneous hyperglycemia risk.

4. The method of claim 1, comprising:

discounting the instantaneous hypoglycemia risk or the instantaneous hyperglycemia risk if the slope of the BG signal indicates that the BG signal is trending towards a midpoint of the blood glucose target range.

5. The method of claim 1, comprising:

summing the hypoglycemia risk profile and the hyperglycemia risk profile to generate a total risk profile (trp).

6. The method of claim 5, comprising:

using the hypoglycemia risk profile to generate an actionable hypoglycemia risk; and

using the hyperglycemia risk profile to generate an actionable hyperglycemia risk.

7. The method of claim 6, wherein the actionable hypoglycemia risk is generated by the algorithm ARlow(s)=lop(s)*[lop(s)−

hip(s)]⁺/trp(s), and the actionable hyperglycemia risk is generated by the algorithm ARhigh(s)=hip(s)*[hip(s)−

lop(s)]⁺/trp(s).

8. The method of claim 7, wherein the unaddressable risk is generated by the algorithm:

UR(s)=[trp(s)−

ARlow(s)−

ARhigh(s)]/2.

9. The method of claim 1, comprising:

interpolating the BG signal to fill gaps in the BG signal.

10. The method of claim 1, comprising:

using the BG signal only for real-time determination of actionable risk and unaddressable risk.

11. The method of claim 1, comprising:

using the BG signal for retrospective analysis of actionable risk and unaddressable risk.

12. A system for assessing actionable glycemic risk comprising:

a digital processor;

a continuous blood glucose monitoring (BG) sensor in communication with the digital processor, the continuous blood glucose monitoring (BG) sensor being configured to generate a blood glucose signal;

a risk determination module, configured to;

receive the glucose signal from the continuous glucose monitoring sensor, and generate an actionable hypoglycemia risk signal, an actionable hyperglycemia risk signal, and an unaddressable risk signal using a glycemic cost function algorithm that attributes equal cost to each of two end points (BGtarget,lo, BGtarget,hi) of a desired blood glucose target range,

determine an instantaneous hypoglycemia risk at the time t and an instantaneous hyperglycemia risk at the time t based on the risk signals,

average each of the instantaneous hypoglycemia risk and the instantaneous hyperglycemia risk over a defined interval to determine a moving average hypoglycemia risk and a moving average hyperglycemia risk;

average each of the moving average hypoglycemia risk and the moving average hyperglycemia risk across multiple days to generate a hypoglycemia risk profile (lop(s)) and a hyperglycemia risk profile (hip(s)) at time s of the day; and

an insulin device that administers insulin to a patient based on the generated hypoglycemia risk profile (lop(s)) and hyperglycemia risk profile (hip(s)).

13. The system of claim 12, wherein the glycemic cost function algorithm is:

14. The system of claim 12, wherein the risk determination module is configured to:

discount the instantaneous hypoglycemia risk or the instantaneous hyperglycemia risk when the slope of the BG signal indicates that the BG signal is trending towards a midpoint of the blood glucose target range.

15. The system of claim 12, wherein the risk determination module is configured to:

sum the hypoglycemia risk profile and the hyperglycemia risk profile to generate a total risk profile (trp).

16. The system of claim 15, wherein the risk determination module is configured to:

a. generate an actionable hypoglycemia risk signal based on the hypoglycemia risk profile by an algorithm ARlow(s)=lop(s)*[lop(s)−

hip(s)]⁺/trp(s);

b. generate an actionable hyperglycemia risk signal based on the hyperglycemia risk profile by an algorithm ARhigh(s)=hip(s)*[hip(s)−

lop(s)] ⁺/trp(s); and

c. generate an unaddressable risk signal by an algorithm UR(s)=[trp(s)−

ARlow(s)−

ARhigh(s)]/2.

17. The system of claim 12, comprising:

an insulin pump in communication with the digital processor and configured to dispense insulin in response to real-time analysis of at least one of the actionable hypoglycemia risk signal, the actionable hyperglycemia risk signal, and the unaddressable risk signal.