GLYCEMIC HEALTH METRIC DETERMINATION AND APPLICATION

• US 20120172694A1
• Filed: 12/15/2011
• Published: 07/05/2012
• Est. Priority Date: 12/29/2010
• Status: Active Grant

• Current Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Inventors: Palerm, Cesar C., Desborough, Lane
• Granted Patent: US 9,445,757 B2
• Time in Patent Office: Days
• US Class Current: 600/365
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• 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 5/6849 in combination with a needle set
• 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: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• 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

Disclosed are methods, apparatuses, etc. for determination and application of a unidimensional metric for assessing a patient'"'"'s glycemic health. In one particular implementation, a computed metric may be used to balance short-term and long-term risks associated with a particular therapy. In another implementation, a computed unidimensional metric may be used to balance risks of hyperglycemia and hypoglycemia.

Claims

1. A method comprising:

at a special purpose computing apparatus, computing a profile of a blood glucose concentration of a patient based, at least in part, on observations of said blood glucose concentration collected at a blood glucose monitoring device;

applying a cost or loss function to said computed profile to compute a metric representative of a glycemic health of the patient, said cost or loss function being based, at least in part, on a log-square metric; and

affecting a therapy applied to said patient based, at least in part, on said computed metric.

2. The method of claim 1, wherein said metric comprises a unidimensional metric.

3. The method of claim 1, wherein affecting said therapy comprises setting a target blood glucose level or target blood glucose range of said patient based, at least in part, on said computed metric.

4. The method of claim 1, wherein said blood glucose concentration profile comprises an estimated probability distribution function.

5. The method of claim 1, wherein said blood glucose concentration profile comprises a histogram of blood glucose concentration measurements.

6. The method of claim 1, wherein said blood glucose concentration profile comprises blood glucose concentration measurements.

7. The method of claim 1, wherein said computed metric comprises a bi-dimensional metric, and further comprising:

applying a first portion of said loss or cost function to said blood glucose concentration profile to derive a hyperglycemia component of said computed metric; and

applying a second portion of said loss or cost function to said blood glucose concentration to derive a hypoglycemia component of said computed metric.

8. The method of claim 1, wherein said log-square metric has the form

9. The method of claim 1, and further comprising:

defining the blood glucose concentration profile for a recurring period;

computing said metric based, at least in part, said recurring period; and

affecting said therapy for said recurring period in the future based, at least in part, on said computed metric.

10. The method of claim 9, wherein said recurring period comprises a time of day.

11. The method of claim 9, wherein said recurring period comprises a day of week.

12. The method of claim 9, wherein said defining said blood glucose concentration profile for a recurring period further comprises computing said profile for the recurring period based on observations of said blood glucose concentration collected only during said recurring period.

13. The method of claim 1, wherein affecting the therapy applied to said patient further comprises affecting a closed-loop insulin delivery system based, at least in part, on said computed metric.

14. The method of claim 13, wherein affecting said closed-loop insulin delivery system further comprises affecting a periodic command based, at least in part, on said computed metric.

15. The method of claim 1, and further comprising triggering an alarm in response to said computed metric.

16. An apparatus comprising:

one or more processors to;

for a plurality of patients, determine an associated plurality of unidimensional metrics indicative of glycemic health of the patients, said unidimensional metrics being computed based, at least in part, on an application of a cost or loss function to profiles of blood glucose concentration of said patients; and

rank said patients for treatment according to a triage policy based, at least in part, on said unidimensional metrics.

17. The apparatus of claim 16, and further comprising communication interface components to receive information from a communication network, said one or more processors further to:

compute said unidimensional metrics based, at least in part, on messages received through said communication interface components from computing platforms co-located with said patients.

18. The apparatus of claim 17, wherein said messages comprise measurements of blood glucose concentration collected at glucose monitoring devices.

19. The apparatus of claim 17, wherein said messages comprise unidimensional metrics computed at said computing platforms co-located with said patients.

20. An article comprising:

a non-transitory storage medium comprising machine-readable instructions stored thereon which are executable by a special purpose computing apparatus to;

determine an associated plurality of unidimensional metrics indicative of glycemic health of the patients, said unidimensional metrics being computed based, at least in part, on an application of a cost or loss function to profiles of blood glucose concentration of said patients; and

rank said patients for treatment according to a triage policy based, at least in part, on said unidimensional metrics.

21. An article comprising:

a non-transitory storage medium comprising machine-readable instructions stored thereon which are executable by a special purpose computing apparatus to;

compute a profile of a blood glucose concentration of a patient based, at least in part, on observations of said blood glucose concentration collected at a blood glucose monitoring device;

apply a cost or loss function to said computed profile to compute a metric representative of a glycemic health of the patient, said cost or loss function being based, at least in part, on a log-square metric; and

affect a therapy applied to said patient based, at least in part, on said computed metric.

22. The article of claim 21, wherein said instructions are executable by said special purpose computing apparatus to affect said therapy by generating commands in an infusion system.

23. An article comprising:

a non-transitory storage medium comprising machine-readable instructions stored thereon which are executable by a special purpose computing apparatus to;

for a plurality of patients, determine an associated plurality of unidimensional metrics indicative of glycemic health of the patients, said unidimensional metrics being computed based, at least in part, on an application of a cost or loss function to profiles of blood glucose concentration of said patients; and

rank said patients for treatment according to a triage policy based, at least in part, on said unidimensional metrics.

24. An apparatus comprising:

means for computing a profile of a blood glucose concentration of a patient based, at least in part, on observations of said blood glucose concentration;

means for applying a cost or loss function to said computed profile to compute a metric representative of a glycemic health of the patient, said cost or loss function being based, at least in part, on a log-square metric; and

means for affecting a therapy applied to said patient based, at least in part, on said computed metric.

25. An apparatus comprising:

for a plurality of patients, means for determining an associated plurality of unidimensional metrics indicative of glycemic health of the patients, said unidimensional metrics being computed based, at least in part, on an application of a cost or loss function to profiles of blood glucose concentration of said patients; and

means for ranking said patients for treatment according to a triage policy based, at least in part, on said unidimensional metrics.

26. A method comprising:

at a special purpose computing apparatus, for a plurality of patients, determining an associated plurality of unidimensional metrics indicative of glycemic health of the patients, said unidimensional metrics being computed based, at least in part, on an application of a cost or loss function to profiles of blood glucose concentration of said patients, said profiles of blood glucose concentration being obtained for said patients under multiple predefined therapies; and

ranking said predefined therapies based, at least in part, on said unidimensional metrics.

27. The method of claim 26, wherein said predefined therapies are defined, at least in part, by closed-loop system design features.