Glycemic health metric determination and application

• US 10,390,740 B2
• Filed: 12/10/2012
• Issued: 08/27/2019
• Est. Priority Date: 12/29/2010
• Status: Active Grant

• Current Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Inventors: Desborough, Lane, Palerm, Cesar, Monirabbasi, Salman
• Primary Examiner(s): Mallari, Patricia
• Assistant Examiner(s): Catina, Michael A
• Application Number:
  US13/710,122
• Publication Number:
  US 20130102867A1
• Time in Patent Office: 2,451 Days
• 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 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/1451 for interstitial fluid
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1473 invasive, e.g. introduced into the body by a catheter A61B5/1482 takes precedence
• 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 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• 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 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.

Claims

1. A method using one or more indicators of glycemic health in a patient to balance one or more of hypoglycemic or hyperglycemic tendencies for the patient, the method comprising:

at a special purpose computing apparatus, computing a profile of a blood glucose concentration of the patient based, at least in part, on a probability density function of a plurality of blood glucose concentration values collected at a blood glucose monitoring device;

determining a metric based, at least in part, on a convolution of a cost or loss function and the profile, wherein the metric is for balancing short-term and long-term risks associated with a therapy, and wherein the metric is representative of a glycemic health of the patient, the cost or loss function being based, at least in part, on a log-square operation and being further based, at least in part, on a parameter selected to balance one or more Spearman rank coefficients with one or more hypoglycemic-based metrics and one or more hyperglycemic-based metrics, wherein the log-square operation is applied to at least one of a computed mean of the plurality of blood glucose concentration values or a measure of a statistical dispersion of the plurality of blood glucose concentration values; and

affecting an insulin therapy applied to the patient by an infusion device, the affected insulin therapy determined based, at least in part, on the metric, wherein the affected insulin therapy is also based, at least part, on a target blood glucose concentration level or target blood glucose range that has been adjusted upward or downward based, at least in part, on an indication by the metric of whether the patient is tending towards hypoglycemia or tending towards hyperglycemia.

2. The method of claim 1, wherein the measure of the statistical dispersion comprises a standard deviation.

3. The method of claim 1, wherein the metric comprises a unidimensional metric.

4. The method of claim 1, wherein affecting the insulin therapy comprises adjusting the target blood glucose level or the target blood glucose range of the patient upward based, at least in part, on the indication by the metric that the patient is tending towards hypoglycemia, or adjusting the target blood glucose level or the target blood glucose range of the patient downward based, at least in part, on the indication by the metric that the patient is tending towards hyperglycemia.

5. The method of claim 1, wherein the log-square operation is further applied to a target blood glucose concentration value.

7. The method of claim 1, wherein the metric is indicative of a portion of time the blood glucose concentration has been within a predetermined range of blood glucose concentration over a duration.

8. The method of claim 7, wherein the predetermined range of blood glucose concentration is 80-180 mg/dl.

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

10. The method of claim 9, wherein affecting the closed-loop insulin delivery system further comprises affecting a periodic command based, at least in part, on the metric.

11. The method of claim 1, and further comprising triggering an alarm in response to the metric.

12. The method of claim 1, wherein the patient is a first patient, the method further comprising:

for each of a plurality of patients, (i) determining a profile of a blood glucose concentration of the patient based, at least in part, on a probability density function of a plurality of blood glucose concentration values collected at one or more additional blood glucose monitoring devices, and (ii) determining a metric based, at least in part, on a convolution of a cost or loss function and the profile, wherein the metric is for balancing short-term and long-term risks associated with a therapy, and wherein the metric is representative of a glycemic health of the patient, the cost or loss function being based, at least in part, on a log-square operation and being further based, at least in part, on a parameter selected to balance one or more Spearman rank coefficients with one or more hypoglycemic-based metrics and one or more hyperglycemic-based metrics, wherein the log-square operation is applied to at least one of a computed mean of the plurality of blood glucose concentration values or a measure of a statistical dispersion of the plurality of blood glucose concentration values; and

ranking the first patient and the plurality of patients for treatment according to a triage policy based, at least in part, on the metrics.

13. The method of claim 12, wherein the determining of the metrics representative of the glycemic health of the first patient and the plurality of patients is

based, at least in part, on messages received through communication interface components from a communication network.

14. The method of claim 13, wherein the messages comprise measurements of blood glucose concentration collected at the one or more additional blood glucose monitoring devices.

21. The method of claim 1, wherein the metric is also based, at least in part, on:

a concentration of hemoglobin A_1c, J-index, glycemic risk assessment diabetes equation (GRADE), M-value (e.g., using 100 mg/dl blood glucose concentration as an ideal), versions of the index of glycemic control (e.g., denoted in specific examples as IGC1 and IGC2 herein), coefficient of variation (CV), Kovatchev'"'"'s low/high blood glucose index (LB GI, HBGI) and total risk index (RI=LBGI+HBGI), % Hypo (e.g., <

70 mg/dl), % Hyper (e.g., >

140 mg/dl), AUC below 70 mg/dl, AUC above 140 mg/dl, total AUC_hypo, AUC_hyper, AUC), interquartile range, Cameron et al.'"'"'s loss function, or a combination thereof.

6. A method using one or more indicators of glycemic health in a patient to balance one or more of hypoglycemic or hyperglycemic tendencies for the patient, the method comprising:

at a special purpose computing apparatus, computing a profile of a blood glucose concentration of the patient based, at least in part, on a probability density function of a plurality of blood glucose concentration values collected at a blood glucose monitoring device;

determining a metric based, at least in part, on a convolution of a cost or loss function and the profile, wherein the metric is for balancing short-term and long-term risks associated with a therapy and is representative of a glycemic health of the patient, and wherein the loss or cost function substantially has the form;

Loss(G_T)=log₁₀(σ

)²+[log 10(μ

)−

log₁₀(G_T)]², where;

G_T is a target blood glucose concentration value;

μ

is a computed mean of the plurality of blood glucose concentration values; and

σ

is a measure of the statistical dispersion of the plurality of blood glucose concentration values; and

affecting an insulin therapy applied to the patient by an infusion device, the affected insulin therapy determined based, at least in part, on the metric, wherein the affected insulin therapy is also based, at least part, on a target blood glucose concentration level or target blood glucose range that has been adjusted upward or downward based, at least in part, on an indication by the metric of whether the patient is tending towards hypoglycemia or tending towards hyperglycemia.

22. The method of claim 6, wherein the patient is a first patient, the method further comprising:

for each of a plurality of patients, (i) determining a profile of a blood glucose concentration of the patient based, at least in part, on a probability density function of a plurality of blood glucose concentration values collected at one or more additional blood glucose monitoring devices, and (ii) determining a metric based, at least in part, on a convolution of a cost or loss function and the profile, wherein the metric is for balancing short-term and long-term risks associated with a therapy and is representative of a glycemic health of the patient, and wherein the cost or loss function substantially has the form;

Loss(G_T)=log₁₀(σ

)²+[log 10(μ

)−

log₁₀(G_T)]², where;

G_T is a target blood glucose concentration value;

μ

is a computed mean of the plurality of blood glucose concentration values; and

σ

is a measure of a statistical dispersion of the plurality of blood glucose concentration values; and

ranking the first patient and the plurality of patients for treatment according to a triage policy based, at least in part, on the metrics.

15. A method performed by a special purpose computing apparatus comprising one or more processors, the method comprising:

for each of a plurality of patients, (i) at the special purpose computing apparatus determining, using the one or more processors, a profile of a blood glucose concentration of the patient based, at least in part, on a probability density function of a plurality of blood glucose concentration values collected at a blood glucose monitoring device, and (ii) at the special purpose computing apparatus determining, using the one or more processors, a metric based, at least in part, on a convolution of a cost or loss function and the profile, wherein the metric is for balancing short-term and long-term risks associated with a therapy, and wherein the metric is representative of a glycemic health of the patient, the cost or loss function being based, at least in part, on a log-square operation and being further based, at least in part, on a parameter selected to balance one or more Spearman rank coefficients with one or more hypoglycemic-based metrics and one or more hyperglycemic-based metrics, wherein the log-square operation is applied to at least one of a computed mean of the plurality of blood glucose concentration values or a measure of a statistical dispersion of the plurality of blood glucose concentration values, and wherein the profiles of the plurality of patients are obtained under multiple predefined therapies; and

ranking, using the one or more processors, the multiple predefined therapies based, at least in part, on the metrics, wherein the ranking of the multiple predefined therapies is used by the one or more processors to control an insulin delivery system for balancing hypoglycemic and hyperglycemic tendencies in the plurality of patients.

16. The method of claim 15, wherein the multiple predefined therapies are defined, at least in part, by closed-loop system design features.

17. The method of claim 15, wherein the measure of the statistical dispersion comprises a standard deviation.

18. The method of claim 15, wherein the log-square operation is further applied to a target blood glucose concentration value.

20. The method of claim 15, wherein the metric is indicative of a portion of time the blood glucose concentration has been within a predetermined range of blood glucose concentration over a duration.

19. A method performed by a special purpose computing apparatus comprising one or more processors, the method comprising:

for each of a plurality of patients, (i) at the special purpose computing apparatus determining, using the one or more processors, a profile of a blood glucose concentration of the patient based, at least in part, on a probability density function of a plurality of blood glucose concentration values collected at a blood glucose monitoring device, and (ii) at the special purpose computing apparatus determining, using the one or more processors, a metric based, at least in part, on a convolution of a cost or loss function and the profile, wherein the metric is for balancing short-term and long-term risks associated with a therapy and is representative of a glycemic health of the patient, wherein the profiles of the plurality of patients are obtained under multiple predefined therapies, and wherein the loss or cost function substantially has the form;

Loss(G_T)=log₁₀(σ

)²+[log 10(μ

)−

log₁₀(G_T)]², where;

G_T is a target blood glucose concentration value;

μ

is a computed mean of the plurality of blood glucose concentration values; and

σ

is a measure of the statistical dispersion of the plurality of blood glucose concentration values; and

ranking, using the one or more processors, the multiple predefined therapies based, at least in part, on the metrics, wherein the ranking of the multiple predefined therapies is used by the one or more processors to control an insulin delivery system for balancing hypoglycemic and hyperglycemic tendencies in the plurality of patients.