Method, system, and computer program product for the evaluation of glycemic control in diabetes from self-monitoring data

• US 20030212317A1
• Filed: 09/26/2002
• Published: 11/13/2003
• Est. Priority Date: 03/29/2000
• Status: Active Grant

• Current Assignee: University of Virginia Patent Foundation (University of Virginia)
• Original Assignee: University of Virginia Patent Foundation (University of Virginia)
• Inventors: Cox, Daniel J., Kovatchev, Boris P.
• Granted Patent: US 7,025,425 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: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A method, system, and computer program product related to the diagnosis of diabetes, and is directed to predicting the long term risk of hyperglycemia, and the long-term and short-term risks of severe hypoglycemia in diabetics, based on blood glucose readings collected by a self-monitoring blood glucose device. The method, system, and computer program product pertain directly to the enhancement of existing home blood glucose monitoring devices, by introducing an intelligent data interpretation component capable of predicting both HbA_1c and periods of increased risk of hypoglycemia, and to the enhancement of emerging continuous monitoring devices by the same features. With these predictions the diabetic can take steps to prevent the adverse consequences associated with hyperglycemia and hypoglycemia.

Claims

1. A computerized method for evaluating the HbA_1c of a patient based on BG data collected over a predetermined duration, said method comprising:

computing weighted deviation toward high blood glucose (WR) and estimated rate of change of blood glucose (Dr) based on said collected BG data; and

estimating HbA_1c using a predetermined mathematical formula based on said computed WR and Dr.

2. The method of claim 1, wherein:

said computed WR is mathematically defined from a series of BG readings x₁, x₂, . . . x_n taken at time points t₁, t₂, . . . , t_n as;

$\mathrm{WR}=\frac{1}{n}<br><br>\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>w<br><br>\text{\hspace{1em}<br><br>}<br><br>r<br><br>\left(x_i;<br><br>1\right)$

where;

wr(BG;

b)=10.f(BG)^b if f(BG)>

0 and 0 otherwise,

b=1, representing a weighting parameter, and

said computed DR is mathematically defined as;

Dr=average of s_k+1−

s_k,

where;

s_k=10.S(k+t₁)² for k=0, 1, . . . , t_n−

t₁,

S(t_j)=f(x_j), for j=1, . . . , n.

3. The method of claim 1, wherein said the estimate of HbA_1c, from said BG monitoring data is mathematically defined as:

Estimated HbA_1c=0.9008(WR)−

0.8207(DR)+6.7489.

4. The method of claim 1, further comprising:

defining predetermined categories for the estimate of HbA_1c, each of said HbA_1c estimate categories representing a range of values for estimated HbA_1c; and

assigning said estimated HbA_1c to at least one of said HbA_1c estimate categories.

5. The method of claim 4, wherein said HbA_1c estimate categories are defined as follows:

classified category 1, wherein said estimated HbA_1c is less than about 7.8;

classified category 2, wherein said estimated HbA_1c is between about 7.8 and about 8.5;

classified category 3, wherein said estimated HbA_1c is between about 8.5 and about 9.0;

classified category 4, wherein said estimated HbA_1c is between about 9.0 and about 9.6;

classified category 5, wherein said estimated HbA_1c is between about 9.6 and about 10.3;

classified category 6, wherein said estimated HbA_1c is between about 10.3 and about 11.0; and

classified category 7, wherein said estimated HbA_1c is above about 11.0.

6. The method of claim 5, further comprising:

defining predicted confidence intervals for corresponding said HbA_1c estimate categories, wherein said predicted confidence intervals are defined as follows;

said classified category 1 corresponds with a predicted HbA_1c less than about 8.0;

said classified category 2 corresponds with a predicted HbA_1c between about 8.0 and about 8.5;

said classified category 3 corresponds with a predicted HbA_1c between about 8.5 and about 9.0;

said classified category 4 corresponds with a predicted HbA_1c between about 9.0 and about 9.5;

said classified category 5 corresponds with a predicted HbA_1c between about 9.5 and about 10.1;

said classified category 6 corresponds with a predicted HbA_1c between about 10.1 and about 11.0; and

said classified category 7 corresponds with a predicted HbA_1c above about 11.0.

7. The method of claim 4, further comprising:

defining predicted confidence intervals for corresponding said HbA_1c, each of said predicted confidence intervals representing a range of values for HbA_1c.

8. The method of claim 7, wherein said predicted HbA_1c confidence intervals have about a 95% confidence level.

9. A computerized method for evaluating the HbA_1c of a patient based on BG data collected over a predetermined duration, said method comprising:

computing weighted deviation toward high blood glucose (WR) and estimated rate of change of blood glucose (Dr) based on said collected BG data;

estimating HbA_1c using a predetermined mathematical formula based on said computed WR and Dr; and

providing a predetermined confidence interval for classification of said estimated value of HbA_1c.

10. The method of claim 9, wherein:

said confidence interval is between about 85% to about 95%.

11. A system for evaluating HbA_1c of a patient based on BG data collected over a predetermined duration, said system comprising:

a database component operative to maintain a database identifying said BG data;

a processor programmed to;

compute weighted deviation toward high blood glucose (WR) and estimated rate of change of blood glucose (Dr) based on said collected BG data; and

estimate HbA_1c using a predetermined mathematical formula based on said computed WR and Dr.

12. The system of claim 11, wherein:

said computed WR is mathematically defined from a series of BG readings x₁, x₂, . . . x_n taken at time points t₁, t₂, . . . , t_n as;

$\mathrm{WR}=\frac{1}{n}<br><br>\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>w<br><br>\text{\hspace{1em}<br><br>}<br><br>r<br><br>\left(x_i;<br><br>1\right)$

where;

wr(BG;

b)=10.f(BG)^b if f(BG)>

0 and 0 otherwise,

b=1, representing a weighting parameter, and

said computed DR is mathematically defined as;

Dr=average of s_k+1−

s_k,

where;

s_k=10.S(k+t₁)² for k=0, 1, . . . , t_n−

t₁,

S(t_j)=f(x_j), for j=1, . . . ,n.

13. The system of claim 11, wherein said the estimate of HbA_1c from said BG monitoring data is mathematically defined as:

Estimated HbA_1c=0.9008(WR)−

0.8207(DR)+6.7489.

14. The system of claim 11, wherein said processor being further programmed to:

define predetermined categories for the estimate of HbA_1c, each of said HbA_1c estimate categories representing a range of values for estimated HbA_1c; and

assign said estimated HbA_1c to at least one of said HbA_1c estimate categories.

15. The system of claim 14, wherein said HbA_1c estimate categories are defined as follows:

classified category 1, wherein said estimated HbA_1c is less than about 7.8;

classified category 2, wherein said estimated HbA_1c is between about 7.8 and about 8.5;

classified category 3, wherein said estimated HbA_1c is between about 8.5 and about 9.0;

classified category 4, wherein said estimated HbA_1c is between about 9.0 and about 9.6;

classified category 5, wherein said estimated HbA_1c is between about 9.6 and about 10.3;

classified category 6, wherein said estimated HbA_1c is between about 10.3 and about 11.0; and

classified category 7, wherein said estimated HbA_1c is above about 11.0.

16. The system of claim 15, wherein said processor being further programmed to:

define predicted confidence intervals for corresponding said HbA_1c estimate categories, wherein said predicted confidence intervals are defined as follows;

said classified category 1 corresponds with a predicted HbA_1c less than about 8.0;

said classified category 2 corresponds with a predicted HbA_1c between about 8.0 and about 8.5;

said classified category 3 corresponds with a predicted HbA_1c between about 8.5 and about 9.0;

said classified category 4 corresponds with a predicted HbA_1c between about 9.0 and about 9.5;

said classified category 5 corresponds with a predicted HbA_1c between about 9.5 and about 10.1;

said classified category 6 corresponds with a predicted HbA_1c between about 10.1 and about 11.0; and

said classified category 7 corresponds with a predicted HbA_1c above about 11.0.

17. The system of claim 14, wherein said processor being further programmed to:

define predicted confidence intervals for corresponding said HbA_1c, each of said predicted confidence intervals representing a range of values for HbA_1c.

18. The system of claim 17, wherein said predicted HbA_1c confidence intervals have about a 95% confidence level.

19. A glycemic control system for evaluating HbA_1c of a patient, said system comprising:

a BG acquisition mechanism, said acquisition mechanism configured to acquire BG data from the patient,

a database component operative to maintain a database identifying said BG data;

a processor programmed to;

compute weighted deviation toward high blood glucose (WR) and estimated rate of change of blood glucose (Dr) based on said collected BG data; and

estimate HbA_1c using a predetermined mathematical formula based on said computed WR and Dr.

20. A computer program product comprising a computer useable medium having computer program logic for enabling at least one processor in a computer system to evaluate HbA_1c of a patient based on BG data, said computer program logic comprising:

computing weighted deviation toward high blood glucose (WR) and estimated rate of change of blood glucose (Dr) based on said collected BG data; and

estimating HbA_1c using a predetermined mathematical formula based on said computed WR and Dr.

21. The computer program product of claim 20, wherein said computer program logic further comprises:

providing a predetermined confidence interval for classification of said estimated value of HbA_1c, wherein said confidence interval is a single value or a range of values.

22. A computerized method for evaluating the long term probability for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration, said method comprising:

computing weighted deviation toward low blood glucose (WL) and estimated rate of fall of blood glucose in the low BG range (DrDn) based on said collected BG data; and

estimating the number of future SH episodes using a predetermined mathematical formula based on said computed WL and DrDn.

23. The method of claim 22, wherein:

said computed WL is mathematically defined from a series of BG readings x₁, x₂, . . . x_n taken at time points t₁, t₂, . . . , t_n as;

$\mathrm{WL}=\frac{1}{n}<br><br>\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>w<br><br>\text{\hspace{1em}<br><br>}<br><br>l<br><br>\left(x_i;<br><br>2\right)<br><br>\text{\hspace{1em}<br><br>}$

where;

wl(BG;

a)=10.f(BG)^a if f(BG)>

0 and 0 otherwise,

a=2, representing a weighting parameter, and said computed DR is mathematically defined as;

DrDn=average of s_k+1−

s_k, provided that s_k<

s_k+1,

where;

s_k=10.S(k+t₁)² for k=0, 1, . . . , t_n-t₁,

S(t_j)=f(x_j), for j=1, . . . , n.

24. The method of claim 22, wherein said estimated number of future SH episodes (EstNSH) is mathematically defined as:

EstNSH=3.3613(WL)−

4.3427(DrDn)−

1.2716.

25. The method of claim 22, further comprising:

defining predetermined EstNSH categories, each of said EstNSH categories representing a range of values for EstNSH; and

assigning said EstNSH to at least one of said EstNSH categories.

26. The method of claim 25, wherein said EstNSH categories are defined as follows:

category 1, wherein said EstNSH category is less than about 0.775;

category 2, wherein said EstNSH category is between about 0.775 and about 3.750;

category 3, wherein said EstNSH category is between about 3.750 and about 7.000; and

category 4, wherein said EstNSH category is above about 7.0.

27. The method of claim 26, further comprising:

defining a probability of incurring a select number of SH episodes respectively for each of said assigned EstNSH categories;

wherein said probability and said respective select number of SH are defined as;

said classified category 1 corresponds with about a 90% probability of incurring about 0 SH episodes and about a 10% probability of incurring about 1 or more SH episodes over the predetermined duration;

said classified category 2 corresponds with about a 50% probability of incurring about 0 SH episodes, 25% probability of incurring about 1 to about 2 SH episodes, and 25% probability of incurring more than 2 SH episodes over the predetermined duration;

said classified category 3 corresponds with about a 25% probability of incurring about 0 SH episodes, 25% probability of incurring about 1 to about 2 SH episodes, and 50% probability of incurring more than 2 SH episodes over the predetermined duration; and

said classified category 4 corresponds with about a 20% probability of incurring about 0 to about 2 SH episodes and about a 80% probability of incurring more than 2 SH episodes over the predetermined duration.

28. The method of claim 25, further comprising:

defining a probability of incurring a select number of SH episodes respectively for each of said assigned EstNSH categories; and

providing at least one probability of incurring a select number of SH episodes according to said EstNSH category to which said EstNSH is assigned.

29. A computerized method for evaluating the long term probability for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration, said method comprising:

computing weighted deviation toward low blood glucose (WL) and estimated rate of fall of blood glucose in the low BG range (DrDn) based on said collected BG data;

estimating the number of future SH episodes using a predetermined mathematical formula based on said computed WL and DrDn; and

defining a probability of incurring a select number of SH episodes respective to said estimated SH episodes.

30. A system for evaluating the long term probability for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration, said system comprising:

a database component operative to maintain a database identifying said BG data;

a processor programmed to;

computing weighted deviation toward low blood glucose (WL) and estimated rate of fall of blood glucose in the low BG range (DrDn) based on said collected BG data; and

estimating the number of future SH episodes using a predetermined mathematical formula based on said computed WL and DrDn.

31. The system of claim 30, wherein:

said computed WL is mathematically defined from a series of BG readings x₁, x₂, . . . x_n taken at time points t₁, t₂, . . . , t_f as;

$\mathrm{WL}=\frac{1}{n}<br><br>\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>w<br><br>\text{\hspace{1em}<br><br>}<br><br>l<br><br>\left(x_i;<br><br>2\right)<br><br>\text{\hspace{1em}<br><br>}$

where;

wl(BG;

a)=10.f(BG)^a if f(BG)>

0 and 0 otherwise,

a=2, representing a weighting parameter, and said computed DR is mathematically defined as;

DrDn=average of s_k+1−

s_k, provided that S_k<

S_k+1,

where;

s_k=10.S(k+t₁)² for k=0, 1, . . . , t_n−

t₁,

S(t_j)=f(x_j), for j=1, . . . ,n.

32. The system of claim 30, wherein said estimated number of future SH episodes (EstNSH) is mathematically defined as:

EstNSH=3.3613(WL)−

4.3427(DrDn)−

1.2716.

33. The system of claim 30, wherein said processor being further programmed to:

define predetermined EstNSH categories, each of said EstNSH categories representing a range of values for EstNSH; and

assign said EstNSH to at least one of said EstNSH categories.

34. The system of claim 33, wherein said EstNSH categories are defined as follows:

category 1, wherein said EstNSH category is less than about 0.775;

category 2, wherein said EstNSH category is between about 0.775 and about 3.750;

category 3, wherein said EstNSH category is between about 3.750 and about 7.000; and

category 4, wherein said EstNSH category is above about 7.0.

35. The method of claim 34, wherein said processor being further programmed to:

define a probability of incurring a select number of SH episodes respectively for each of said assigned EstNSH categories, wherein said probability and said respective select number of SH are defined as;

said classified category 1 corresponds with about a 90% probability of incurring about 0 SH episodes and about a 10% probability of incurring about 1 or more SH episodes over the predetermined duration;

said classified category 2 corresponds with about a 50% probability of incurring about 0 SH episodes, 25% probability of incurring about 1 to about 2 SH episodes, and 25% probability of incurring more than 2 SH episodes over the predetermined duration;

said classified category 3 corresponds with about a 25% probability of incurring about 0 SH episodes, 25% probability of incurring about 1 to about 2 SH episodes, and 50% probability of incurring more than 2 SH episodes over the predetermined duration; and

said classified category 4 corresponds with about a 20% probability of incurring about 0 to about 2 SH episodes and about a 80% probability of incurring more than 2 SH episodes over the predetermined duration.

36. The system of claim 33, wherein said processor being further programmed to:

define a probability of incurring a select number of SH episodes respectively for each of said assigned EstNSH categories; and

provide at least one probability of incurring a select number of SH episodes according to said EstNSH category to which said EstNSH is assigned.

37. A glycemic control system for evaluating the long term probability for severe hypoglycemia (SH) of a patient, said system comprising:

a BG acquisition mechanism, said acquisition mechanism configured to acquire BG data from the patient, a database component operative to maintain a database identifying said BG data;

a processor programmed to;

computing weighted deviation toward low blood glucose (WL) and estimated rate of fall of blood glucose in the low BG range (DrDn) based on said collected BG data; and

estimating the number of future SH episodes using a predetermined mathematical formula based on said computed WL and DrDn.

38. A computer program product comprising a computer useable medium having computer program logic for enabling at least one processor in a computer system to evaluate long term probability for severe hypoglycemia (SH) of a patient based on BG data, said computer program logic comprising:

computing weighted deviation toward low blood glucose (WL) and estimated rate of fall of blood glucose in the low BG range (DrDn) based on said collected BG data; and

estimating the number of future SH episodes using a predetermined mathematical formula based on said computed WL and DrDn.

39. The computer program product of claim 38, wherein said computer program logic further comprises:

defining a probability of incurring a select number of SH episodes respective to said estimated SH episodes.

40. A computerized method for evaluating the short term risk for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration, said method comprising:

computing weighted deviation toward low blood glucose (WL);

determining Max(wl) by calculating maximum value of wl(BG;

2); and

determining risk value by taking the geometric mean of WL and Max(wl) over said predetermined duration, said risk value is mathematically defined as;

$\mathrm{risk}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{value}=\sqrt{\mathrm{WL}·<br><br>\mathrm{Max}<br><br>\left(\mathrm{wl}\right)}.$

41. The method of claim 40, wherein:

said computed WL is mathematically defined from a series of BG readings x₁, x₂, . . . x_n taken over the predetermined duration as;

$\mathrm{WL}=\frac{1}{n}<br><br>\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>w<br><br>\text{\hspace{1em}<br><br>}<br><br>l<br><br>\left(x_i;<br><br>2\right)<br><br>\text{\hspace{1em}<br><br>}$

where;

wl(BG;

a)=10.f(BG)^a if f(BG)>

0 and 0 otherwise,

a=2, representing a weighting parameter.

42. The method of claim 40, further comprising:

providing a predetermined threshold risk value; and

comparing said determined risk value to said threshold risk value.

43. The method of claim 42, wherein:

if said determined risk value is greater than said threshold value then short term risk of incurring a hypoglycemic episode is high; and

if said determined risk value is less than said threshold value then short term risk of incurring a hypoglycemic episode is low.

44. The method of claim 43, wherein said short term is approximately a 24 hour period.

45. The method of claim 43, wherein said short term ranges from about 12 to about 72 hour period.

46. The method of claim 43, wherein said threshold value is approximately 17.

47. The method of claim 43, wherein said threshold value is between about 12 to 25.

48. A system for evaluating the short term risk for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration, said system comprising:

a database component operative to maintain a database identifying said BG data;

a processor programmed to;

compute weighted deviation toward low blood glucose (WL);

determine Max(wl) by calculating maximum value of wl(BG;

2); and

determine risk value by taking the geometric mean of WL and Max(wl) over said predetermined duration, said risk value is mathematically defined as;

$\mathrm{risk}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{value}=\sqrt{\mathrm{WL}·<br><br>\mathrm{Max}<br><br>\left(\mathrm{wl}\right)}.$

49. The system of claim 48, wherein:

said computed WL is mathematically defined from a series of BG readings x₁, x₂, . . . x_n taken over the predetermined duration as;

$\mathrm{WL}=\frac{1}{n}<br><br>\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>w<br><br>\text{\hspace{1em}<br><br>}<br><br>l<br><br>\left(x_i;<br><br>2\right)<br><br>\text{\hspace{1em}<br><br>}$

where;

wl(BG;

a)=10.f(BG)^a if f(BG)>

0 and 0 otherwise,

a=2, representing a weighting parameter.

50. The system of claim 48, wherein said processor being further programmed to:

provide a predetermined threshold risk value; and

compare said determined risk value to said threshold risk value.

51. The system of claim 50, wherein:

if said determined risk value is greater than said threshold value then short term risk of incurring a hypoglycemic episode is high; and

if said determined risk value is less than said threshold value then short term risk of incurring a hypoglycemic episode is low.

52. The system of claim 51, wherein said short term is approximately a 24 hour period.

53. The method of claim 51, wherein said short term ranges from about 12 to about 72 hour period.

54. The system of claim 51, wherein said threshold value is approximately 17.

55. The method of claim 51, wherein said threshold value is between about 12 to 25.

56. A glycemic control system for evaluating the short term risk for severe hypoglycemia (SH) of a patient, said system comprising:

a BG acquisition mechanism, said acquisition mechanism configured to acquire BG data from the patient, a database component operative to maintain a database identifying said BG data;

a processor programmed to;

compute weighted deviation toward low blood glucose (WL);

determine Max(wl) by calculating maximum value of wl(BG;

2); and

determine risk value by taking the geometric mean of WL and Max(wl) over said predetermined duration, said risk value is mathematically defined as;

$\mathrm{risk}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{value}=\sqrt{\mathrm{WL}·<br><br>\mathrm{Max}<br><br>\left(w<br><br>\text{\hspace{1em}<br><br>}<br><br>l\right)}.$

57. A computer program product comprising a computer useable medium having computer program logic for enabling at least one processor in a computer system to evaluate the short term risk for severe hypoglycemia (SH) of a patient based on BG data collected over a predetermined duration, said computer program logic comprising:

computing weighted deviation toward low blood glucose (WL);

determining Max(wl) by calculating maximum value of wl(BG;

2); and

determining risk value by taking the geometric mean of WL and Max(wl) over said predetermined duration, said risk value is mathematically defined as;

$\mathrm{risk}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{value}=\sqrt{\mathrm{WL}·<br><br>\mathrm{Max}<br><br>\left(w<br><br>\text{\hspace{1em}<br><br>}<br><br>l\right)}.$

58. The computer program product of claim 57, wherein said computer program logic further comprises:

providing a predetermined threshold risk value; and

comparing said determined risk value to said threshold risk value.