Method of adapting in-vitro models to aid in noninvasive glucose determination

• US 7,317,938 B2
• Filed: 10/29/2004
• Issued: 01/08/2008
• Est. Priority Date: 10/08/1999
• Status: Expired due to Fees

• Current Assignee: GLT Acquisition Corporation
• Original Assignee: Sensys Medical, Inc. (Sensys Medical Limited)
• Inventors: Ruchti, Timothy L., Lorenz, Alexander D.
• Primary Examiner(s): WINAKUR, ERIC FRANK
• Application Number:
  US10/978,116
• Publication Number:
  US 20050119541A1
• Time in Patent Office: 1,166 Days
• Field of Search: 600/310, 600/316, 600/322
• US Class Current: 600/316
• CPC Class Codes: A61B 2560/0233 Optical standards
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: G01N 21/278 Constitution of standards
• CPC Class Codes: G01N 21/359 using near infrared light

Abstract

The invention relates to a noninvasive analyzer and a method of using information determined at least in part from in-vitro spectra of tissue phantoms or analyte solutions to aid in the development of a noninvasive glucose concentration analyzer and/or in the analysis of noninvasive spectra resulting in glucose concentration estimations in the body. The preferred apparatus is a spectrometer that includes a base module and a sample module that is semi-continuously in contact with a human subject and that collects spectral measurements which are used to determine a biological parameter in the sampled tissue, such as glucose concentration. Collection of in-vitro samples is, optionally, performed on a separate instrument from the production model allowing the measurement technology to be developed on a research grade instrument and used or transferred to a target product platform or production analyzer for noninvasive glucose concentration estimation.

Claims

1. A computer implemented method for noninvasively estimating an analyte concentration with an in-vivo instrument system, comprising the steps of:

providing a first model;

removing at least one interference from said first model to form a second model;

standardizing said in-vivo instrument system to said second model to generate a third model;

providing an in-vivo test set, comprising;

at least one in-vivo test signal; and

a reference analyte concentration corresponding with said in-vivo test signal;

applying said third model to said in-vivo test signal to generate a test value;

applying a correction to said third model using said test value and said reference analyte concentration to yield a corrected third model;

providing an in-vivo measurement signal; and

estimating and providing for use said analyte concentration using said corrected third model and said in-vivo measurement signal.

2. The method of claim 1, wherein said first model comprises coefficients generated at least in part with an in-vitro data set.

3. The method of claim 2, wherein said third model comprises a third set of coefficients.

4. The method of claim 3, wherein said analyte concentration comprises a glucose concentration.

5. The method of claim 4, wherein said first model comprises coefficients derived from data comprised of at least twenty percent in-vitro data.

6. The method of claim 5, wherein said first model comprises coefficients derived from data comprised of at least eighty percent in-vitro data.

7. The method of claim 4, wherein said step of applying a correction comprises the step of at least one of:

applying an offset; and

applying a scaling factor.

8. The method of claim 7, further comprising:

repeating said steps of providing an in-vivo test set, applying said second model to said in-vivo test set, applying a correction, and applying an offset.

9. The method of claim 3, wherein said step of removing comprises projecting said coefficients onto a null space of said interference.

10. The method of claim 9, wherein said interference comprises at least one of:

a protein signal;

a fat signal;

a water signal;

a salt signal;

a thermal noise;

a specific tissue sample spectrum;

an individual;

a class of subjects; and

a cluster of data.

11. The method of claim 3, wherein said step of standardizing comprises the step of at least one of:

smoothing;

interpolating;

scaling;

filtering;

performing an offset correction;

performing a bias correction;

normalizing;

performing direct standardization

performing piece-wise direct standardization;

performing a standard normal variate transformation;

performing multiplicative scatter correction;

performing orthogonal signal correction;

re-sampling; and

correcting wavelength.

12. The method of claim 11, wherein said step of standardizing comprises at least three of:

smoothing;

interpolating;

scaling;

filtering;

performing offset correction

performing bias correction;

normalizing;

performing direct standardization

performing piece-wise direct standardization;

performing standard normal variate transformation;

performing multiplicative scatter correction;

performing orthogonal signal correction;

re-sampling; and

correcting wavelength.

13. The method of claim 3, wherein said in-vivo test signal comprises a spectrum.

14. The method of claim 3, wherein said second model comprises coefficients generated on a first instrument and said third model comprises coefficients generated on a second instrument.

15. The method of claim 14, wherein said first instrument comprises a research grade spectrometer.

16. The method of claim 14, wherein said second instrument comprises a production grade analyzer.

17. The method of claim 3, wherein said in-vivo measurement signal comprises a spectrum.

18. The method of claim 3, further comprising the step of:

housing at least one of said first model, said second model, and said third model in an analyzer.

19. The method of claim 18, further comprising the step of:

providing an analyzer that comprises;

a base module;

a communication bundle with a first end and a second end, wherein said first end is connected to said base module;

a sample module, wherein said second end of said communication bundle is connected to said sample module; and

a processor.

20. A computer implemented method for noninvasive estimation of a sample constituent property, comprising the steps of:

providing a noninvasive signal;

providing a first model, wherein said first model comprises coefficients that are generated at least in part with an in-vitro data set, wherein said in-vitro data set comprises a spectrum of a tissue phantom having at least one optical parameter representative of said noninvasive signal in terms of photonic scattering and/or absorbance;

standardizing an in-vivo instrument system to said first model, wherein a second model is generated; and

estimating and providing for use said sample property by applying said second model to said noninvasive signal.

21. The method of claim 20, wherein said step of standardizing comprises the step of at least one of:

smoothing;

interpolating;

scaling;

filtering;

performing offset correction

performing bias correction;

normalizing;

performing direct standardization

performing piece-wise direct standardization;

performing standard normal variate transformation;

performing multiplicative scatter correction;

performing orthogonal signal correction;

re-sampling; and

correcting wavelength.

22. The method of claim 21, wherein said step of standardizing comprises the step of at least three of:

smoothing;

interpolating;

scaling;

filtering;

performing offset correction

performing bias correction;

normalizing;

performing direct standardization

performing piece-wise direct standardization;

performing standard normal variate transformation;

performing multiplicative scatter correction;

performing orthogonal signal correction;

re-sampling; and

correcting wavelength.

23. The method of claim 20, wherein said sample property comprises a glucose concentration.

24. The method of claim 20, wherein said data set comprises data that are collected with a first analyzer, and said noninvasive signal comprises a signal that is collected with a second analyzer.

25. The method of claim 24, wherein said first analyzer comprises a research grade instrument.

26. The method of claim 24, wherein said second analyzer comprises a production analyzer.

27. The method of claim 24, wherein said second analyzer comprises a base module in a first container and a sample module in a second container.

28. The method of claim 20, wherein said first model is generated with at least eighty percent in-vitro data.

29. An apparatus for noninvasive estimation of a sample constituent property from a noninvasive spectrum, comprising:

an analyzer comprising a base module, a sample module, and a model residing in said analyzer;

wherein said model comprises coefficients generated by standardizing an in-vivo system to an in-vitro data set,

wherein said in-vitro data set comprises a spectrum of a tissue phantom having at least one optical parameter representative of said noninvasive spectrum in terms of photonic scattering and/or absorbance; and

wherein said model is applied to said noninvasive spectrum for estimation of said sample constituent property.

30. The apparatus of claim 29, wherein said base module resides in a first container and said sample module resides in a second container.

31. A computer implemented method for noninvasive estimation of a sample constituent property, comprising the steps of:

providing a first model, wherein said first model comprises coefficients that are generated at least in part with an in-vitro data set;

standardizing an in-vivo instrument system to said first model to generate a second model, wherein said second model comprises a second set of coefficients;

providing an in-vivo test set, comprising;

at least one in-vivo test signal; and

a reference sample concentration that is correlated with said in-vivo test signal;

applying said second model to said in-vivo test set to generate a test value;

providing an in-vivo measurement signal; and

estimating and providing for use said sample constituent property using said second model and said in-vivo measurement signal, wherein said step of estimating comprises multiplication of said in-vivo measurement signal by both a regression vector and a scaling factor resulting in a product that is adjusted with an offset.

32. The method of claim 31, wherein said test signal comprises a spectrum.

33. The method of claim 31, wherein said sample constituent property comprises a glucose concentration.

34. The method of claim 33, further comprising the step of:

repeating said steps of providing an in-vivo test set, applying said second model to said in-vivo test set, applying a correction, and applying an offset.

35. The method of claim 33, wherein said estimated glucose concentration is determined according to:

ŷ

=xaW+b

where a is said scaling factor, b is said offset, x is said in-vivo test measurement signal, W is said regression vector of said second model, and ŷ

is said estimated glucose concentration.

36. The method of claim 31, wherein said first model comprises coefficients generated using a first instrument and said test signal comprises signals generated on a second instrument.

37. The method of claim 36, wherein said first instrument comprises a research grade spectrometer.

38. The method of claim 36, wherein said second instrument comprises a production grade spectrometer.

39. The method of claim 31, wherein said first model comprises coefficients generated with a research grade spectrometer.

40. The method of claim 31, wherein said in-vivo instrument system comprises a production grade analyzer.

41. The method of claim 31, wherein said first model is generated with at least twenty percent in-vitro data.

42. The method of claim 41, wherein said first model is generated with at least eighty percent in-vitro data.

43. The method of claim 31, wherein said step of standardizing comprises the step of at least one of:

smoothing;

interpolating;

scaling;

filtering;

performing offset correction

performing bias correction;

normalizing;

performing direct standardization

performing piece-wise direct standardization;

performing standard normal variate transformation;

performing multiplicative scatter correction;

performing orthogonal signal correction;

re-sampling; and

correcting wavelength.

44. A computer implemented method for noninvasively estimating an analyte concentration, comprising the steps of:

providing a first calibration model;

removing at least one interference from said first model to form a second model, wherein said first model comprises coefficients derived from data comprised of at least twenty percent in-vitro data, wherein said step of removing comprises projecting said coefficients onto a null space of said interference;

providing an in-vivo signal; and

estimating and providing for use said analyte concentration using said second model and said in-vivo signal.

45. The method of claim 44, wherein said first model comprises coefficients derived from data comprised of at least eighty percent in-vitro data.

46. The method of claim 44, wherein said analyte concentration comprises a glucose concentration.

47. The method of claim 44, wherein said step of removing comprises subtraction.

48. The method of claim 44, wherein said interference comprises at least one of:

protein;

fat;

a specific tissue sample;

an individual;

a class of subjects; and

a cluster of data.

49. An apparatus for noninvasive estimation of a sample constituent property from a noninvasive spectrum, comprising:

an analyzer, comprising;

a base module;

a sample module; and

a model residing in said analyzer;

wherein said model comprises coefficients generated by standardizing an in-vivo system to a model of coefficients derived at least in part from an in-vitro data set;

wherein said in-vitro signal comprises a spectrum of a tissue phantom having at least one optical parameter representative of said noninvasive spectrum in terms of photonic scattering and/or absorbance; and

wherein said model is applied to said noninvasive spectrum to generate said sample constituent property.

50. The apparatus of claim 49, wherein said model further comprises a correction to said model.

51. The apparatus of claim 50, wherein said correction comprises at least one of:

an offset; and

a scaling factor.

52. The apparatus of claim 49, wherein said base module resides in a first container and said sample module resides in a second container.

53. The apparatus of claim 52, further comprising:

a communication bundle;

wherein said communication bundle interfaces said base module to said sample module.

54. A computer implemented method for noninvasively estimating a blood/tissue glucose concentration, comprising the steps of:

providing a noninvasive near-infrared signal;

providing a calibration model;

supplementing said calibration model with an in-vitro signal, wherein said in-vitro signal comprises a spectrum of a tissue phantom having at least one optical parameter representative of said noninvasive near-infrared signal in terms of photonic scattering and/or absorbance; and

estimating and providing for use said blood glucose concentration using said model and said noninvasive signal.

55. The method of claim 54, further comprising the steps of:

providing an in-vivo test set, comprising;

at least one in-vivo test signal; and

a reference glucose concentration that is correlated with said in-vivo test signal;

applying said model to said in-vivo test signal to generate a test value; and

determining a correction to said model using said test value and said reference glucose concentration.

56. The method of claim 55, further comprising the step of:

repeating said steps of providing an in-vivo test set;

applying said model to said in-vivo test signal to generate a test value; and

determining a correction to said model using said test value and said reference glucose concentration.

57. The method of claim 56, further comprising the step of:

removing at least one interference from said model.

58. A computer implemented method for noninvasive estimation of a sample constituent property from an in-vivo instrument system, comprising the steps of:

providing a noninvasive signal;

providing a model, comprising coefficients generated at least in part with an in-vitro data set, wherein said in-vitro data set comprises a spectrum of a tissue phantom having at least one optical parameter representative of said noninvasive spectrum in terms of photonic scattering and/or absorbance;

standardizing said model to said in-vivo instrument system; and

estimating and providing for use said sample property by applying said model to said noninvasive signal.

59. The method of claim 58, wherein said step of standardizing comprises the step of at least one of:

smoothing;

interpolating;

scaling;

filtering;

performing offset correction

performing bias correction;

normalizing;

performing direct standardization

performing piece-wise direct standardization;

performing standard normal variate transformation;

performing multiplicative scatter correction;

performing orthogonal signal correction;

re-sampling; and

correcting wavelength.

60. The method of claim 59, wherein said step of standardizing comprises the step of at least three of:

smoothing;

interpolating;

scaling;

filtering;

performing offset correction

performing bias correction;

normalizing;

performing direct standardization

performing piece-wise direct standardization;

performing standard normal variate transformation;

performing multiplicative scatter correction;

performing orthogonal signal correction;

re-sampling; and

correcting wavelength.

61. The method of claim 58, wherein said sample property comprises a glucose concentration.

62. The method of claim 58, wherein said data set comprises data that are collected with a first analyzer and said noninvasive signal comprises a signal that is collected with a second analyzer.

63. The method of claim 62, wherein said first analyzer comprises a research grade instrument.

64. The method of claim 62, wherein said second analyzer comprises a production analyzer.

65. The method of claim 62, wherein said second analyzer comprises a base module residing a first container and a sample module residing in a second container.

66. The method of claim 58, wherein said model is generated with at least eighty percent in-vitro data.