Surgery – Diagnostic testing – Measuring or detecting nonradioactive constituent of body...
Reexamination Certificate
2002-08-01
2004-09-07
Winakur, Eric F. (Department: 3736)
Surgery
Diagnostic testing
Measuring or detecting nonradioactive constituent of body...
C600S322000, C600S310000, C600S473000, C600S476000
Reexamination Certificate
active
06788965
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to noninvasive blood and tissue analyte determination. More particularly, the invention relates to methods and apparatus for detecting conditions leading to erroneous noninvasive tissue analyte measurements.
2. Description of Related Art
Previously we reported an invention for measuring glucose noninvasively through an intelligent measurement system (IMS) in S. Malin, T. Ruchti, An intelligent system for noninvasive blood analyte prediction, U.S. Pat. No. 6,280,381 (Aug. 28, 2001). The IMS involved the classification of patients into a multiplicity of “bins” or “classes” and the application of a suitable calibration model. A key element of the IMS is a Performance Monitor capable of detecting poor instrument performance, patient sampling errors, and other anomalies leading to an invalid or degraded glucose measurement. Here we describe a novel method for detecting and mitigating the wide range of potential errors associated with the in vivo application of an instrument for the noninvasive measurement of glucose.
The error detection system (EDS) operates on a near infrared measurement of in vivo skin tissue. The architecture employs a pattern classification engine and hierarchy of levels to analyze, detect, and diagnose instrument, interface, and sample errors manifested in the near infrared measurement. A priori information about the sources of errors is used to establish preset limits and categories of errors. Application of the system results in improved noninvasive glucose measurement accuracy through the rejection of invalid and poor samples.
Noninvasive Measurement of Glucose
Diabetes is a leading cause of death and disability worldwide and afflicts an estimated 16 million Americans. Complications of diabetes include heart and kidney disease, blindness, nerve damage, and high blood pressure with the estimated total cost to United States economy alone exceeding $90 billion per year [
Diabetes Statistics,
Publication No. 98-3926, National Institutes of Health, Bethesda Md. (November 1997)]. Long-term clinical studies show that the onset of complications can be significantly reduced through proper control of blood glucose levels [The Diabetes Control and Complications Trial Research Group,
The effect of intensive treatment of diabetes on the development and progression of long
-
term complications in insulin
-
dependent diabetes mellitus.
N Eng J of Med, 329:977-86 (1993)]. A vital element of diabetes management is the self-monitoring of blood glucose levels by diabetics in the home environment. A significant disadvantage of current monitoring techniques is that they discourage regular use due to the inconvenient and painful nature of drawing blood through the skin prior to analysis. Therefore, new methods for self-monitoring of blood glucose levels are required to improve the prospects for more rigorous control of blood glucose in diabetic patients.
Numerous approaches have been explored for measuring glucose levels in vivo, ranging from invasive methods such as micro dialysis to noninvasive technologies that rely on spectroscopy. Each method has associated advantages and disadvantages, but only a few have received approval from certifying agencies. To date, no noninvasive techniques for the self-monitoring of blood glucose have been certified.
One method, near-infrared spectroscopy involves the illumination of a spot on the body with near-infrared electromagnetic radiation (light in the wavelength range 700-0.2500 nm). The light is partially absorbed and scattered, according to its interaction with the constituents of the tissue prior to being reflected back to a detector. The detected light contains quantitative information that is based on the known interaction of the incident light with components of the body tissue including water, fats, protein, and glucose.
Previously reported methods for the noninvasive measurement of glucose through near-infrared spectroscopy rely on the detection of the magnitude of light attenuation caused by the absorption signature of blood glucose as represented in the targeted tissue volume. The tissue volume is the portion of irradiated tissue from which light is reflected or transmitted to the spectrometer detection system. The signal due to the absorption of glucose is extracted from the spectral measurement through various methods of signal processing and one or more mathematical models. The models are developed through the process of calibration on the basis of an exemplary set of spectral measurements and associated reference blood glucose values (the calibration set) based on an analysis of capillary (fingertip), or venous blood.
Near-infrared spectroscopy has been demonstrated in specific studies to represent a feasible and promising approach to the noninvasive prediction of blood glucose levels. M. Robinson, R. Eaton, D. Haaland, G. Keep, E. Thomas, B. Stalled, P. Robinson, Noninvasive
glucose monitoring in diabetic patients: A preliminary evaluation,
Clin Chem, 38:1618-22 (1992) reports three different instrument configurations for measuring diffuse transmittance through the finger in the 600-1300 nm range. Meal tolerance tests were used to perturb the glucose levels of three subjects and calibration models were constructed specific to each subject on single days and tested through cross-validation. Absolute average prediction errors ranged from 19.8 to 37.8 mg/dL. H. Heise, R. Marbach, T. Koschinsky, F. Gries, Noninvasive
blood glucose sensors based on near
-
infrared spectroscopy,
Artif Org, 18:439-47 (1994); H. Heise, R. Marbach,
Effect of data pretreatment on the noninvasive blood glucose measurement by diffuse reflectance near
-
IR spectroscopy,
SPIE Proc, 2089:114-5 (1994); R. Marbach, T. Koschinsky, F. Gries, H. Heise, Noninvasive
glucose assay by near
-
infrared diffuse reflectance spectroscopy of the human inner lip,
Appl Spectrosc, 47:875-81 (1993) and R. Marbach, H. Heise,
Optical diffuse reflectance accessory for measurements of skin tissue by near
-
infrared spectroscopy,
Applied Optics 34(4):610-21 (1995) present results through a diffuse reflectance measurement of the oral mucosa in the 1111-1835 nm range with an optimized diffuse reflectance accessory. In vivo experiments were conducted on single diabetics using glucose tolerance tests and on a population of 133 different subjects. The best standard error of prediction reported was 43 mg/dL and was obtained from a two-day single person oral glucose tolerance test that was evaluated through cross-validation.
K. Jagemann, C. Fischbacker, K. Danzer, U. Muller, B. Mertes,
Application of near
-
infrared spectroscopy for noninvasive determination of blood/tissue glucose using neural network,
Z Phys Chem, 191 S: 179-190 (1995); C. Fischbacker, K. Jagemann, K. Danzer, U. Muller, L. Papenkrodt, J. Schuler,
Enhancing calibration models for noninvasive near
-
infrared spectroscopic blood glucose
determinations, Fresenius J Anal Chem 359:78-82 (1997); K. Danzer, C. Fischbacker, K. Jagemann, K. Reichelt,
Near
-
infrared diffuse reflection spectroscopy for noninvasive blood
-
glucose monitoring,
LEOS Newsletter 12(2):9-11 (1998); and U. Muller, B. Mertes, C. Fischbacker, K. Jagemann, K. Danzer,
Noninvasive blood glucose monitoring by means of new infrared spectroscopic methods for improving the reliability of the calibration models,
Int J Artif Organs, 20:285-290 (1997) recorded spectra in diffuse reflectance over the 800-1350 nm range on the middle finger of the right hand with a fiber-optic probe. Each experiment involved a diabetic subject and was conducted over a single day with perturbation of blood glucose levels through carbohydrate loading. Results, using both partial least squares regression and radial basis function neural networks were evaluated on single subjects over single days through cross-validation. Danzer, et al., supra, report an average root mean square prediction error of 36 mg/dL through cross-validation over 31 glucose prof
Blank Thomas B.
Briggs Christopher C.
Lorenz Alexander D.
Makarewicz Marcy
Mattu Mutua
Glenn Michael A.
Glenn Patent Group
Sensys Medical Inc.
Winakur Eric F.
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