Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Biological or biochemical
Reexamination Certificate
2002-10-29
2004-07-27
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Biological or biochemical
C600S300000
Reexamination Certificate
active
06768948
ABSTRACT:
FIELD OF INVENTION
The present invention relates to the detection and evaluation of multiple imbalances within multi-parametric systems, particularly to the employment of graphic means for performing such detections and evaluations. It is particularly useful in the field of medicine for the diagnosis and follow-up treatment of diseases. It may be used in many other fields for evaluating, diagnosing, predicting, analyzing, describing behavior, change of behavior, etc., where multiple parameters in a related system are involved.
BACKGROUND OF THE INVENTION
In medicine, for optimal care and therapy, quantitative as well as qualitative judgments of the degrees of abnormalities should be made when diagnosing patients. Previous studies have suggested that an analysis of combinations of laboratory data of a patient may be of greater aid in understanding the patient's condition than an analysis of individual items of data per se.
Heretofore one scientific method of diagnosing diseases from laboratory data has used a statistical analysis of deviations of a patient's data from a normal range. The results obtained were arranged in the form of a circular coordinate system which employed radial axes calibrated according to the patient's laboratory parameters, with standard deviations of each parameter plotted on the respective axes. Following this, a pattern was created by interconnecting adjacent points on the axes. Diagnosis was performed by comparing the obtained pattern of an individual patient with reference patterns typical for certain diseases. J. H. Siegel, “Relations Between Circulatory and Specific Changes in Sepsis,” 32 Ann. Rev. Med. (Annual Reviews, Inc. 1981) 175-194; also see the “Patient Data System,” General Electric Medical Systems (adv't.), Critical Care Medicine, January/February 1976.
While useful, these methods did not provide sufficient information for one to detect pathology with normal data and did not reveal qualitative and quantitative types of imbalances between parameters.
Another method has been suggested in an attempt to overcome these difficulties. This method was similar to the previous ones: a circular type representation of parameters on radial axes was provided with values plotted on the radial axes, but expressed as a percentage of normal values, rather than by standard deviations. S. Nazari et al., “A Multivariable Pattern for Nutritional Assessment,” 4 J. Parenteral and Enteral Nutrition 499, 1980.
This method provided more distinguishable patterns than the previous one because the percentage scale was more sensitive than the standard deviation scale. Nevertheless this method still did not provide sufficient information for one to obtain quantities and qualitative types of imbalances between parameters and did not reveal any multiple imbalances which were present within the system.
In order to overcome disadvantages of the aforementioned known methods and systems, the applicant have developed a new diagnostic system based on so-called balascopic units which is described in U.S. Pat. No. 4,527,240 issued to the applicant in 1985 and incorporated herein by reference. In this system, relationships between multiple related parameters, such as blood chemistry data, are evaluated by converting the data into specially normalized units as a percentage on a scale depicting the maximum and minimum empirical values for such parameter.
The essence of the balascopy consists of transformation of measured values into dimensionless balascopic scales. The balascopic units used in these scales are dimensionless and are based on the following assumptions.
Let us assume that P
max
is the maximum value ever measured for a certain parameter, e.g., serum protein and that that P
min
is the minimum value ever measured for serum protein. These values are obtained from the existing data based on large amounts of available measurements. Let us designate the difference between P
max
and P
min
as &xgr;, i.e.,
P
max
−P
min
=&xgr;=Const (mg/dl)
Let us introduce an inverse scale based on 100 units based on the following transformation:
100/&xgr;=&eegr;=Const (dl/mg).
The measured value of the parameter is designated as P
mes
(mg/dl). The same parameter obtained from the available statistical data for healthy people is designated as P
mes (st.norm)
.
Let us subtract P
min
from P
mes
and designate the result of subtraction as &ngr;, i.e.,
P
mes
−P
min
=&ngr;(mg/dl)
It is understood that for a live person u is always greater than 0. A balascopic unit P
bu
, on which the previous and the present invention of applicant are based, is equal to:
P
bu
=(100/&xgr;)·&ngr;(dimensionless)
P
bu
is always less than 100.
It is obvious that the value of P
bu
corresponding to &ngr;
norm
=P
mes(st. norm)
−P
min
, i.e., P
bu norm
.=(100/&xgr;)·&ngr;
norm
is an average statistical value of a selected parameter for a healthy person, e.g., of serum protein, expressed in balascopic units.
A main advantage of transfer to balascopic units is the use of large statistic data obtained for healthy and unhealthy people (P
max
and P
min
).
For example, the empirically existing maximum of the total serum protein in vivo comprises 11.0 milligrams (mg) of protein per tenth liter (deciliter—dl) of blood, and the minimum is 2.0 mg/dl. The range between these values is thus 11.0-2.0=9.0 mg/dl. This range is then converted into special normalized units on a scale of 100, such that each normalized unit will correspond to 100/9=11.1 actual units (in mg/dl). A patient's measured total serum protein value may be thus converted to normalized units by subtracting the minimum actual value from the patient's actual value and then multiplying the result by 11.1 or by 100/9.
For example, if a patient's measured total serum protein is 7.3 mg/dl, this value is made the minuend, the minimum empirical value (2.0 mg/dl), is made the subtrahend, and the difference, 5.3 mg/dl, is determined. This difference (5.3 mg/dl) is then multiplied by the normalized unit value, 11.1, to provide a special normalized value according to the invention, which is 58.9 units.
The applicant have designated these special normalized units (regardless of the parameter represented) by the term Balascopic units, where “bala” stems from the word “balanced” and “scopic” stems from the Greek word “observe”. Thus it is clear that the balascopic method is based on visual representation of deviation from balance.
Then a normal relationship between pairs of such data (FIG.
1
—N) is provided and compared with measured relationships between corresponding pairs of data (FIG.
1
—CN to FN) and quantitative and qualitative evaluations are made. Also the complete set of data for such a system is plotted on respective radial axes in such normalized units on a circular coordinate system with the respective maximum and minimum for each parameter being marked on its radius. This is shown in
FIG. 2
which is a circular diagram of a balascopic pattern for blood chemistry. This diagram contains a closed-loop contour
22
plotted for maximum values of the parameters and a closed loop contour
21
for minimum values of the parameters, while a normal closed-loop pattern, which is completely within the area defined between the contours
20
and
21
and which is P
bu st.norm
, is an average statistical value for normal parameters of a healthy person (FIG.
2
). Then measured parameters for various entities are similarly plotted and compared with the normal annulus or known abnormal annuli (
FIG. 3
is a diagram of the type shown in
FIG. 2
for
Diabetes mellitius
with Kimmel Stiel-Wilson disease and Secondary Hyperparathyroidism. It is understood that similar diagrams can be plotted for other diseases such as myxedema, thyrotoxicosis, etc. with deviation patterns typical of each specific disease.
Generally, laboratory data or measured parameters of a patient are used to make and confirm a diagnosis and to monitor the course of treatment. In a bas
Charioui Mohamed
Hoff Marc S.
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