Near infrared blood glucose monitoring system

• US 20020193671A1
• Filed: 08/17/2001
• Published: 12/19/2002
• Est. Priority Date: 08/21/2000
• Status: Active Grant

• Current Assignee: Euroceltique SA
• Original Assignee: Euroceltique SA
• Inventors: Bynum, Kevin P., Ciurczak, Emil W., Mark, Howard
• Granted Patent: US 6,675,030 B2
• Time in Patent Office: Days
• US Class Current: 600/316
• 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: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: G01J 2003/1213 Filters in general, e.g. dichroic, band
• CPC Class Codes: G01J 2003/1828 with order sorter or prefilter
• CPC Class Codes: G01J 2003/2866 Markers; Calibrating of scan
• CPC Class Codes: G01J 3/02 Details
• CPC Class Codes: G01J 3/0232 using shutters
• CPC Class Codes: G01J 3/0235 using means for replacing an element by another, for replacing a filter or a grating
• CPC Class Codes: G01J 3/0237 Adjustable, e.g. focussing
• CPC Class Codes: G01J 3/0254 Spectrometers, other than colorimeters, making use of an integrating sphere
• CPC Class Codes: G01J 3/0289 Field-of-view determination; Aiming or pointing of a spectrometer; Adjusting alignment; Encoding angular position; Size of measurement area; Position tracking
• CPC Class Codes: G01J 3/10 Arrangements of light sources specially adapted for spectrometry or colorimetry
• CPC Class Codes: G01J 3/12 Generating the spectrum; Monochromators
• CPC Class Codes: G01J 3/28 Investigating the spectrum using colour filters G01J3/51
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 2201/065 Integrating spheres

Abstract

An individualized modeling equation for predicting a patient'"'"'s blood glucose values is generated as a function of non-invasive spectral scans of a body part and an analysis of blood samples from the patient, and is stored on a central computer. The central computer predicts a blood glucose value for the patient as a function of the individualized modeling equation and a non-invasive spectral scan generated by a remote spectral device. If the spectral scan falls within the range of the modeling equation, the predicted blood glucose level is output to the patient. If the spectral scan falls outside the range of the modeling equation, regeneration of the model is required, and the patient takes a number of noninvasive scans and an invasive blood glucose level determination. The computer regenerates the individualized modeling equation as a function of the set of spectral scans and corresponding blood glucose values.

Claims

1. A method for predicting blood glucose values in a patient comprising:

(a) generating an individualized modeling equation for a patient as a function of non-invasive spectral scans of a body part of the patient and an analysis of blood samples from the patient and storing the individualized modeling equation on a central computer;

(b) receiving, from the patient, a non-invasive spectral scan generated by a remote spectral device;

(c) predicting a blood glucose value for the patient as a function of the non-invasive spectral scan and the individualized modeling equation, and transmitting the predicted blood glucose value to the patient;

(d) determining that a regeneration of the individualized modeling equation is required, and transmitting a request for a set of non invasive spectral scans and a corresponding set of blood glucose values to the patient;

(e) acquiring a set of noninvasive spectral scans from the patient using the remote spectral device and a corresponding set of blood glucose values from a remote invasive blood glucose monitor;

(f) transmitting the set of spectral scans and corresponding blood glucose values to the central computer;

(g) regenerating the individualized modeling equation as a function of the set of spectral scans and corresponding blood glucose values.

3. The method of claim 1, wherein the remote spectral device includes an infrared spectrometer.

4. The method of claim 3, wherein the infrared spectrometer includes a grating spectrometer, a diode array spectrometer, a filter-type spectrometer, an Acousto Optical Tunable Filter spectrometer, a scanning spectrometer, and a nondispersive spectrometer.

6. The method of claim 1, wherein the remote spectral device is handheld.

7. The method of claim 1, wherein the invasive method includes taking a blood sample by a venipuncture, a fingerstick, and a heelstick.

8. The method of claim 7, wherein the blood sample is inserted into an analytical device.

9. The method of claim 1, wherein the spectral device communicates with the central computer by a mode of data transmission.

10. The method of claim 9, wherein the mode of data transmission is one of a cellular data link, a telephone modem, a direct satellite link, an Internet link, and an RS232 data connection.

11. The method of claim 1, wherein the spectral device is a sensor, a monitor and a handheld computer.

14. The method of claim 13, wherein the regeneration is conducted about every four weeks.

15. The method of claim 1, wherein the spectral device detects nonspectral body properties.

16. The method of claim 1, wherein the spectral device controls administering an amount of a drug to the patient.

17. The method of claim 1, wherein the central computer is a workstation capable of holding a plurality of spectral scans and modeling equations for a plurality of patients.

2. An automated method for predicting blood glucose values using a noninvasive spectroscopic technique, comprising the steps of:

(a) taking a plurality of measurements of a patient'"'"'s blood glucose levels using a noninvasive spectral device and an invasive glucose monitoring method;

(b) associating a constituent value measured by the invasive glucose monitoring method with the blood glucose level measured by the spectral device;

(c) dividing the plurality of spectral scans and constituent values into a calibration subset and a validation subset;

(d) transforming the spectral scans in the calibration sub-set and the validation subset by applying a plurality of a first mathematical finction to the calibration sub-set and the validation sub-set to obtain a plurality of transformed validation data sub-sets and a plurality of transformed calibration sub-sets;

(e) resolving each transformed calibration data sub-set in step (d) by at least one of a second mathematical function to generate a plurality of modeling equations; and

(f) selecting a best modeling equation of the plurality of modeling equations;

(g) storing the best modeling equation in a central computer;

(h) acquiring a spectral scan from the patient using a remote noninvasive spectral device;

(i) transmitting the spectral scan from step (h) in the central computer of step (g);

(j) predicting the patient'"'"'s blood glucose level using the best modeling equation; and

(k) regenerating the best modeling equation if the spectral scan falls outside a range for the modeling equation.

5. The method of claim 2, wherein the noninvasive spectral device includes a remote spectral device.

12. The method of claim 2, wherein the measurements are taken daily for about six weeks.

13. The method of claim 2 wherein the regenerating step further comprises the steps of:

(a) taking a plurality of invasive blood glucose levels and noninvasive spectral scans from the patient;

(b) transmitting the blood glucose levels and spectral scans of step (a) to the central computer; and

(c) regenerating the modeling equation using the invasive blood glucose levels and non invasive spectral scans of steps (a) and (b).

18. The method of claim 2, wherein the best modeling equation is selected as a function of calculating a figure of merit (FOM), the FOM being defined as:

19. The method of claim 2, wherein the at least one second mathematical function includes one or more of a partial least squares, a principal component regression, a neural network, and a multiple linear regression analysis.

20. The method of claim 2, wherein the first set of mathematical functions include performing a normalization of the spectral scan, performing a first derivative on the spectral scan, performing a second derivative on the spectral scan, performing a multiplicative scatter correction on the spectral scan, performing smoothing transform on the spectral scan, and performing a Kubelka-Munk fimction on the spectral scan.

21. The method of claim 2, wherein the first set of mathematical functions are applied singularly and two-at-a-time.

22. A method for predicting blood glucose values in an individual patient, comprising

generating, on a central computer, a modeling equation for predicting blood glucose values in a patient and predicting a blood glucose value for the patient based upon a non-invasive spectral scan of the patient with a remote spectral device and the modeling equation;

regenerating, on the central computer, the modeling equation based upon non-invasive spectral scans of the patient with the remote spectral device and corresponding constituent values for the patient based upon an invasive blood glucose measurement with a remote invasive device; and

predicting a blood glucose value for the patient based upon a subsequent non-invasive spectral scan of the patient with the remote spectral device and the regenerated modeling equation.

23. A system for predicting blood glucose values in an individual patent, comprising

a remote non-invasive spectral device, the remote non-invasive spectral device generating a spectral scan of a body part of a patient;

a remote invasive device, the remote invasive device generating a constituent value for the patient;

a central computer, the central computer storing a modeling equation for predicting blood glucose values in a patient, the central computer predicting a blood glucose value for the patient based upon a non-invasive spectral scan of the patient from the remote non-invasive spectral device and the modeling equation, the central computer regenerating the modeling equation based upon a plurality of non-invasive spectral scan of the patient from the remote non-invasive spectral device and a corresponding plurality of constituent values for the patient from the remote invasive device, and predicting a blood glucose value for the patient based upon a subsequent non-invasive spectral scan of the patient with the remote non-invasive spectral device and the regenerated modeling equation.