U.S. patent application number 10/694122 was filed with the patent office on 2004-04-01 for system and method for analyzing antibiotic susceptibility of biological samples.
Invention is credited to Clyde, Merlise, O'Connell, Michael A., Parmigiani, Giovanni, Turner, David J., Wiles, Timothy M..
Application Number | 20040063168 10/694122 |
Document ID | / |
Family ID | 24335021 |
Filed Date | 2004-04-01 |
United States Patent
Application |
20040063168 |
Kind Code |
A1 |
Wiles, Timothy M. ; et
al. |
April 1, 2004 |
System and method for analyzing antibiotic susceptibility of
biological samples
Abstract
A system and method for analyzing samples, such as biological
samples, to accurately and effectively determine the susceptibility
of the samples to antimicrobial materials, so as to determine
minimum inhibitory concentration (MIC) values for the respective
samples and antimicrobial materials. At each of a plurality of time
intervals, the system and method directs a plurality of different
analyzing light wavelengths, such as red, green and blue
wavelengths, onto each of a plurality of sample wells, and detects
a respective resultant light wavelength emanating from the
respective sample wells for each of the analyzing light
wavelengths. The system and method uses resultant light wavelengths
to generate at least two growth indicator characteristic curves
representing, for example, the redox state and turbidity
characteristics of the sample wells. The system then uses the redox
state and turbidity characteristics of sample wells containing the
same antimicrobial material to determine the MIC value for that
material in relation to the sample contained in those wells.
Inventors: |
Wiles, Timothy M.;
(Reisterstown, MD) ; Turner, David J.; (Owings
Mills, MD) ; O'Connell, Michael A.; (Durham, NC)
; Parmigiani, Giovanni; (Baltimore, MD) ; Clyde,
Merlise; (Durham, NC) |
Correspondence
Address: |
BECTON, DICKINSON AND COMPANY
1 BECTON DRIVE
FRANKLIN LAKES
NJ
07417-1880
US
|
Family ID: |
24335021 |
Appl. No.: |
10/694122 |
Filed: |
October 27, 2003 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10694122 |
Oct 27, 2003 |
|
|
|
09583891 |
May 31, 2000 |
|
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Current U.S.
Class: |
435/29 |
Current CPC
Class: |
G01N 21/82 20130101;
G01N 21/6428 20130101; G01N 21/253 20130101; G01N 21/6452 20130101;
G01N 21/78 20130101 |
Class at
Publication: |
435/029 |
International
Class: |
C12Q 001/02 |
Claims
What is claimed is:
1. A method for analyzing a sample contained in at least one sample
well, comprising the steps of: directing a plurality of different
analyzing light wavelengths onto said sample contained in said
sample well; detecting a respective resultant light wavelength
emanating from said sample for each of said analyzing light
wavelengths directed onto said sample; generating a result value
representative of each respective resultant light wavelength; and
mathematically combining said result values to provide at least two
growth indicator values, each representing a respective growth
characteristic of said sample.
2. A method as claimed in claim 1, wherein: one of said growth
indicator values represents a redox state of said sample.
3. A method as claimed in claim 1, wherein: one of said growth
indicator values represents a turbidity value of said sample.
4. A method as claimed in claim 1, wherein: said directing step
directs at least three of said analyzing light wavelengths onto
said sample.
5. A method as claimed in claim 4, wherein: said three analyzing
light wavelengths include red, green and blue light
wavelengths.
6. A method as claimed in claim 1, wherein: said sample is
contained in a plurality of said sample wells; and said directing,
detecting and combining steps are performed for each of said sample
wells.
7. A method as claimed in claim 1, wherein: said directing,
detecting and combining steps are each performed on said sample in
said sample well at a plurality of time intervals, such that each
of said combining steps provides a set of said growth indicator
values for each of said time intervals.
8. A method as claimed in claim 1, wherein: said sample is
contained in a plurality of said sample wells; and said directing,
detecting and combining steps are performed on said sample in each
of said sample wells at each of a plurality of time intervals, such
that each of said combining steps provides a respective set of said
growth indicator values for each of said respective sample wells at
each of said intervals.
9. A method as claimed in claim 8, further comprising the step of:
mathematically combining certain of said values in said respective
sets of growth indicator values for each of said sample wells to
provide a respective sample well characteristic value for each of
said respective sample wells.
10. A method as claimed in claim 9, further comprising the step of:
grouping said sample well characteristic values into a-plurality of
groups; and comparing said sample well characteristic values to
each other in each of said respective groups to determine in which
sample wells in each of said groups sample growth is inhibited.
11. A computer-readable medium of instructions for controlling a
sample analyzing system to analyze a sample contained in at least
one sample well, comprising: a first set of instructions, adapted
to control said system to direct a plurality of different analyzing
light wavelengths onto said sample contained in said sample well; a
second set of instructions, adapted to control said system to
detect a respective resultant light wavelength emanating from said
sample for each of said analyzing light wavelengths directed onto
said sample, and to provide a result value representative of each
respective resultant light wavelength; and a third set of
instructions, adapted to control said system to mathematically
combine said result values to provide at least two growth indicator
values, each representing a respective growth characteristics of
said sample.
12. A computer-readable medium of instructions as claimed in claim
11, wherein: one of said growth indicator values represents a redox
state of said sample.
13. A computer-readable medium of instructions as claimed in claim
11, wherein: one of said growth indicator values represents a
turbidity value of said sample.
14. A computer-readable medium of instructions as claimed in claim
11, wherein: said first set of instructions controls said system to
direct at least three of said analyzing light wavelengths onto said
sample.
15. A computer-readable medium of instructions as claimed in claim
14, wherein: said three analyzing light wavelengths include red,
green and blue light wavelengths.
16. A computer-readable medium of instructions as claimed in claim
11, wherein: said sample is contained in a plurality of said sample
wells; and said first, second and third sets of instructions are
adapted to control said system to perform said directing, detecting
and combining operations for each of said sample wells.
17. A computer-readable medium of instructions as claimed in claim
11, wherein: said first, second and third set of instructions are
adapted to control said system to perform said directing, detecting
and combining operations on said sample in said sample well at a
plurality of time intervals, such that each of said combining
operations provides a set of said growth indicator values for each
of said intervals.
18. A computer-readable medium of instructions as claimed in claim
11, wherein: said sample is contained in a plurality of said sample
wells; and said first, second and third set of instructions are
adapted to control said system to perform said directing, detecting
and combining operations on said sample in each of said sample
wells at each of a plurality of time intervals, such that each of
said combining operations provides a respective set of said growth
indicator values for each of said respective sample wells at each
of said intervals.
19. A computer-readable medium of instructions as claimed in claim
18, further comprising: a fourth set of instructions, adapted to
control said system to mathematically combine certain of said
values in said respective sets of growth indicator values for each
of said sample wells to provide a respective sample well
characteristic value for each of said respective sample wells.
20. A computer-readable medium of instructions as claimed in claim
19, further comprising: a fifth set of instructions, adapted to
control said system to group said sample well characteristic values
into a plurality of groups; and a sixth set of instructions,
adapted to control said system to compare said sample well
characteristic values to each other in each of said respective
groups to determine in which sample wells in each of said groups
sample growth is inhibited.
21. A method for determining at least one minimum inhibitory
concentration (MIC) value for a sample contained in a sample
container, said sample container including a plurality of sample
wells, each containing a portion of said sample and a respective
material adapted to affect growth of said sample, said method
comprising the steps of: taking a respective set of readings of
each respective sample well at each of a plurality of intervals of
time to provide a respective set of values for each respective
sample well at each of said intervals; for each of said sample
wells, mathematically combining said respective sets of values to
provide a respective well characteristic value for each of said
sample wells; grouping said sample well characteristic values into
a plurality of groups representative of respective groups of said
sample wells; and comparing said sample well characteristic values
to each other in each of said respective groups to determine a
respective MIC value for each of said groups of sample wells.
22. A method as claimed in claim 21, wherein: said readings taking
step detects a plurality of light wavelengths from each of said
sample wells at each of said intervals to provide said respective
sets of values for each respective sample well at each of said
intervals.
23. A method as claimed in claim 22, wherein: said plurality of
light wavelengths includes red, blue and green light
wavelengths.
24. A method as claimed in claim 22, wherein: in each of said
respective sets of values, one of said values represents a redox
state of its respective sample well and the other of said values
represents a turbidity value of its respective sample well.
25. A computer-readable medium of instructions for controlling a
sample analyzing system to determine at least one minimum
inhibitory concentration (MIC) value for a sample contained in a
sample container, said sample container including a plurality of
sample wells, each containing a portion of said sample and a
respective material adapted to affect growth of said sample, said
computer-readable medium of instructions comprising: a first set of
instructions, adapted to control said system to take a respective
set of readings of each respective sample well at each of a
plurality of intervals of time to provide a respective set of
values for each respective sample well at each of said intervals; a
second set of instructions, adapted to control said system to, for
each of said sample wells, mathematically combine said respective
sets of values to provide a respective well characteristic value
for each of said sample wells; a third set of instructions, adapted
to control said system to group said sample well characteristic
values into a plurality of groups representative of respective
groups of said sample wells; and a fourth set of instructions,
adapted to control said system to compare said sample well
characteristic values to each other in each of said respective
groups to determine a respective MIC value for each of said groups
of sample wells.
26. A computer-readable medium of instructions as claimed in claim
25, wherein: said first set of instructions controls said system to
detect a plurality of different light wavelengths from each of said
sample wells at each of said intervals to provide said respective
sets of values for each respective sample well at each of said
intervals.
27. A computer readable medium of instructions as claimed in claim
26, wherein: said plurality of light wavelengths includes red, blue
and green light wavelengths.
28. A computer readable medium of instructions as claimed in claim
26, wherein: in each of said respective sets of values, one of said
values represents a redox state of its respective sample well and
the other of said values represents a turbidity value of its
respective sample well.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] Related subject matter is disclosed in a copending U.S.
patent application of Clark et al., entitled "Automated
Microbiological Testing Apparatus and Methods Therefor", Ser. No.
09/083,130, filed May 22, 1998, the entire contents of which are
incorporated herein by reference. This is a divisional of U.S. Ser.
No. 09/583,891 filed May 31, 2000.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates to a system and method for
analyzing samples, such as biological samples, to determine the
susceptibility of the samples to antimicrobial materials, such as
antibiotics. More particularly, the present invention relates to a
system and method which takes a plurality of optical readings of a
biological sample contained in sample wells of a sample test panel
having various types and concentrations of antimicrobial materials
therein and, based on these readings, determines the respective
minimum inhibitory concentrations (MICs) at which the respective
antimicrobial materials will inhibit growth of the sample.
[0004] 2. Description of the Related Art
[0005] Many conventional systems exist for performing tests on
microbiological samples related to patient diagnosis and therapy.
The microbiological samples may come from a variety of sources,
including infected wounds, genital infections, cerebro-spinal
fluids, blood, abscesses or any other suitable source. From the
microorganism samples, an inoculum is prepared in accordance with
established procedures which produce a bacterial or cellular
suspension of a predetermined concentration. Further processing of
the suspension may depend on the testing method employed, as can be
appreciated by one skilled in the art.
[0006] The conventional systems are used, for example, to identify
the types of microorganisms present in a patient's sample.
Typically, in such systems, reagents are placed into cupules, or
test wells, of identification trays, into which the sample is
introduced. The reagents change color in the presence of an
actively growing culture of microorganisms. Based on the color
change, or lack thereof, the microorganism can be identified by the
use of reference tables.
[0007] Other systems have been developed for susceptibility testing
of microorganisms. These systems are used to determine the
susceptibility of a microorganism in a sample to various
therapeutics, such as antibiotics. Based on these test results,
physicians can then, for example, prescribe an antimicrobial
product which will be successful in eliminating or inhibiting
growth of the microorganism. Qualitative susceptibility testing, in
particular, provides an indication of whether a microorganism is
resistant or sensitive to a particular antibiotic, but does not
provide an indication on the degree of sensitivity or resistance of
the microorganism. On the other hand, quantitative susceptibility
testing provides an indication of the concentration of the
antimicrobial agent needed to inhibit growth of the microorganism.
The term minimum inhibitory concentration (MIC) is used to refer to
the minimum concentration of the antimicrobial agent that is
required to inhibit the growth of a microorganism.
[0008] Although the conventional systems can be somewhat useful in
determining the MICs at which respective antimicrobial agents will
inhibit growth of respective microorganisms, these systems have
certain drawbacks. For example, when performing identification and
susceptibility testing, the test trays are incubated at a
controlled temperature for an extended period of time. At
predetermined time intervals, the wells of the test trays are
individually examined for an indication of color change or other
test criteria. However, this process can be long and tedious when
performed manually by a technician. In addition, the incubation
times for identification and susceptibility test trays may differ,
or the optimal time to read a test result from the test tray may
not be known in advance. Thus, a technician may typically need to
read and record results for a specimen at several different times,
sometimes far apart, which may cause assignment or correlation
errors.
[0009] Automated systems are desirable in performing these tests to
minimize the technician handling time, as well as to minimize the
possibility of human error. In addition, automated systems may be
preferred because they generally can obtain results more rapidly
and accurately than manual methods. One known microbiological
testing apparatus for the automatic incubation and reading of
microbiological samples employs a plurality of test trays having a
plurality of wells which contain the samples or agents to be
tested. The trays are first placed in an incubator, and are then
moved to an inspection station after a sufficient incubation
period. A light source is disposed above the tray and a pair of
video cameras are disposed below the tray at the inspection
station. Each video camera takes a video image of an entire tray,
and the video image signal of the entire tray is sent to an image
processor to be analyzed.
[0010] The image processor requires uniform lighting over the
entire inspection station. Consequently, the processor records the
background light level of each pixel within an area of interest
corresponding to each well of the tray to account for variability
in the light source. The image processor processes the video image
of the tray and determines the number of pixels for a particular
well whose intensity exceeds a predetermined threshold for that
area of interest. If the number of pixels exceeds a predetermined
number, a positive result is assigned to that well. The image
processor analyzes the binary partial results from the wells to
determine the possible identity of the microorganisms. The binary
partial results are compared to prerecorded patterns of results for
each type of test tray to identify the sample in question.
[0011] A microbiological testing apparatus for detecting the
presence of a fluorescence emitting reaction resulting from the
interaction of a reacting agent and a sample for detection,
susceptibility, and identification testing, is also known. In this
apparatus, multiple trays having a plurality of test chambers are
contained within a carousel. This carousel is rotated to move one
of the trays close to a detection area. A positioning mechanism
then radially moves that tray out of the carousel and into the
detection area, and a high-energy light source is disposed
proximate to the tray. The light source provides narrow-band light
sufficient to produce an emission fluorescence from the reaction
within the test chambers, which in turn is detected by a video
mechanism disposed opposite to the light source and behind the
positioned tray. The video mechanism produces an image based on the
emission wavelength.
[0012] Another test system is known for identifying bacteria using
signals based on the intensity of monochromatic light reflected
from specimens placed in a culture plate having a plurality of
cells. A rotary disk containing six interference filters is
interposed between a lamp and a group of optical fibers. The light
from the lamp passes through a particular interference filter to
produce monochromatic light of a certain wavelength. The filtered
monochromatic light is guided by the optical fibers to be incident
on respective cells of the culture plate. The disk is rotated so
that the six different wavelength monochromatic lights are caused
to be incident on the cells sequentially. The light reflected from
the specimens is guided by additional optical fibers to
corresponding phototransistors. A signal is derived for each
specimen based on the intensity of the reflected monochromatic
light. These signals are then analyzed to determine the identity of
the specimen by calculating the difference, or ratio, between the
signals and comparing that result with a reference value.
[0013] Although the above-described systems may be somewhat useful,
each system fails to fulfill all of the requirements of a fully
automated microbiological testing system. In particular, the known
systems are not capable of simultaneously performing both
colorimetric-type and fluorometric-type testing on multiple-well
test panels, which is needed to obtain more accurate test results.
Further, these systems are generally not designed to continuously
gather test data from a plurality of multiple-well test panels in a
quick and reliable manner. Moreover, the automated processing of
these systems is limited.
[0014] In addition, the known systems do not examine multiple
indicators of growth of the samples, and then base the MIC
calculations on these multiple growth indicators. The use of data
from multiple growth indicators is desirable to provide increased
accuracy and integrity of the results. Furthermore, the known
systems fail to employ a method of screening questionable MIC
results. In particular, the known systems do not evaluate the
quality and reliability of the MIC results to provide a probability
or confidence value which indicates the level of certainty at which
the MIC results are deemed to be correct.
[0015] Accordingly, a need exists for a system and method for an
improved system and method for analyzing biological samples to
determine the susceptibility of the samples to antimicrobial
materials, and to provide MIC values for the antimicrobial
materials with respect to the various samples.
SUMMARY OF THE INVENTION
[0016] An object of the present invention is to provide a system
and method for analyzing samples, such as biological samples, to
accurately and effectively determine the susceptibility of the
samples to antimicrobial materials.
[0017] Another object of the present invention is to provide a
system and method which measures multiple indicators of growth of
the biological samples, and then uses these measurements to
determine the susceptibility of the samples to the various
antimicrobial materials to provide MIC values for the respective
samples and antimicrobial materials.
[0018] A further object of the invention is to provide a system and
method evaluating the calculated MIC values for respective samples
and antimicrobial materials to provide a probability or confidence
value which indicates the level of certainty at which the MIC
values are deemed to be correct.
[0019] A still further object of the present invention is to
provide a system and method for optically reading a biological
sample contained in sample wells of a sample test panel having
various types and concentrations of antimicrobial materials therein
and, based on these readings, measuring a plurality of growth
indicators of the samples that the system and method uses to
determine the respective minimum inhibitory concentrations (MICs)
at which the respective antimicrobial materials will inhibit growth
of the sample.
[0020] These and other objects of the present invention are
substantially achieved by providing a system and method for
analyzing a sample contained in at least one sample well by
directing a plurality of analyzing light waves of different
wavelengths, such as red, green and blue, onto the sample contained
in the sample well, and detecting a respective resultant light wave
emanating from the sample for each of the analyzing light waves
being directed onto the sample. The system and method then provides
a result value representative of each respective resultant light
wave, and mathematically combines the result values to provide at
least two growth indicator values, such as the redox state and
turbidity of the sample, each of which represents a respective
growth characteristic of the sample. The method and system can
perform the directing, detecting and mathematical combining steps
on the sample in the sample well at a plurality of time intervals,
such that each of the mathematical combining steps performed
provides a set of growth indicator values for each of the time
intervals.
[0021] The method and system can perform the above steps on a
plurality of the sample wells at a plurality of time intervals to
obtain a respective set of growth indicator values for each of the
respective sample wells at each of the time intervals. The method
and system can then further mathematically combine certain of the
growth indicator values in the respective sets of growth indicator
values for each of the sample wells to provide a respective sample
well characteristic value; such as an MIC value, for each of the
respective sample wells. The method and system can then group the
sample well characteristic values into a plurality of groups, and
compare the sample well characteristic values to each other in each
of the respective groups to determine in which sample wells in each
of the groups sample growth is inhibited.
[0022] Another aspect of the present invention lies in providing a
system and method for determining at least one minimum inhibitory
concentration (MIC) value for a sample contained in a sample
container that includes a plurality of sample wells, each of which
containing a portion of the sample and a respective material
adapted to affect growth of the sample. The system and method take
a respective set of readings of each respective sample well at each
of a plurality of intervals of time to provide a respective set of
values for each respective sample well at each of said intervals.
The readings are taken, for example, by detecting a plurality of
light waves of different wavelengths, such as red, green and blue,
from each of the sample wells at each of said intervals to provide
the respective sets of values for each respective sample well at
each of the intervals. Also, in each of the respective sets of
values, one of the values represents a redox state of its
respective sample well and the other value represents a turbidity
value of its respective sample well.
[0023] For each of the sample wells, the system and method
mathematically combine the respective sets of values to provide a
respective well characteristic value for each of the sample wells.
The system and method then group the sample well characteristic
values into a plurality of groups representative of respective
groups of the sample wells, and compare the sample well
characteristic values to each other in each of the respective
groups to determine a respective MIC value for each of the groups
of sample wells.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] These and other objects, advantages and novel features of
the invention will be more readily appreciated from the following
detailed description when read in conjunction with the accompanying
drawings, in which:
[0025] FIG. 1 illustrates a system for analyzing samples to
determine their antimicrobial susceptibility according to an
embodiment of the present invention;
[0026] FIG. 2 illustrates a carousel housing portion of the system
shown in FIG. 1;
[0027] FIG. 3 is a top view of the carousel housing portion shown
in FIG. 2 with the top of the enclosure removed;
[0028] FIGS. 4A-4C are perspective, top and bottom views of an
example of a test panel used in the system shown in FIG. 1;
[0029] FIG. 5 is a diagrammatic view of the sample well reading
components of the system shown in FIG. 1;
[0030] FIG. 6 is a schematic diagram illustrating the
interrelationship among the mechanical and electrical components of
the system shown in FIG. 1;
[0031] FIGS. 7A and 7B are flowcharts showing the steps performed
by the system shown in FIG. 1 for analyzing samples contained in
sample wells of the test panels as shown in FIGS. 4A-4C;
[0032] FIG. 8 is a graph illustrating redox states and turbidity
values for a sample contained in one sample well of a test panel as
calculated by the system shown in FIG. 1;
[0033] FIG. 9 is a graph illustrating the relationship between a
variable and its indication of the probability of growth of a
sample, which is evaluated by the system shown in FIG. 1 to derive
an MIC value for the sample;
[0034] FIG. 10 is a table illustrating an example of MIC values and
probabilities calculated according to an embodiment of the present
invention;
[0035] FIG. 11 is a table illustrating another example of MIC
values and probabilities calculated according to an embodiment of
the present invention; and
[0036] FIG. 12 is a graph illustrating the relationship between
redox values for wells having different antibiotic concentrations
in relation to elapsed incubation time.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0037] FIG. 1 illustrates a system 100 according to an embodiment
of the present invention for analyzing biological samples to
identify the susceptibility of the samples to various types and
concentrations of antimicrobial materials, and for calculating the
minimum inhibitory concentration (MIC) at which the respective
antibiotics or antimicrobial materials inhibit growth of the
respective samples. The system 100 includes a measurement
instrument 102 having an enclosure 104 which is divided into a
carousel housing portion 106 and a controller housing portion 108.
The system 100 further includes a workstation 110, such as a
personal computer (PC) or the like, which is coupled to the
controller housing portion 108 to communicate with the system 100
for purposes of transferring data to and from the system 100, for
example.
[0038] The carousel housing portion 106 includes a door 112 and a
latch mechanism 114. The latch mechanism 114 can maintain the door
112 in a closed state, and can be manipulated to allow the door 112
to be opened to expose an interior chamber 115 of the carousel
housing portion 106. The controller housing portion 108 includes a
display panel 116, a keyboard panel 118, a computer readable medium
drive 120, and a barcode reader 122, the purposes of which are
described in detail below.
[0039] As shown in FIGS. 2 and 3, a carousel 124 is housed in the
interior chamber 115 of the carousel housing portion 106. The
carousel 124 includes a plurality of rings and ribs bolted to a
drive ring 126 to form a cylindrical cage, which is mounted
vertically in the interior chamber 115. The carousel housing
portion 106 is insulated to provide a substantially uniform
temperature incubation environment in the interior chamber 115, and
is light-tight under normal operation to prevent ambient light from
entering the interior chamber 115, as described in more detail in
copending U.S. patent application Ser. No. 09/083,130, referenced
above.
[0040] In this example, the carousel 124 is arranged to include
four horizontal tiers with each tier having twenty-six panel
positions, thus providing a total of one-hundred and four panel
positions 128. However, these numbers of tiers and panel positions
128 may be changed to accommodate the requirements of any specified
application as will be appreciated by one skilled in the art. A
panel carrier 130 is mounted in each of the panel positions 128.
Each panel carrier 130 is configured to receive a test panel 132,
an example of which is shown in FIGS. 4A-4C.
[0041] As shown in FIGS. 4A-4C, a test panel 132 is a disposable,
transparent or semi-transparent device which is inoculated with
materials or reagents needed for both identification (II)) and
antimicrobial susceptibility determination (AST) testing of the
samples. The testing is performed based on reactions that occur
between the samples and reagents placed in individual wells 134 on
each ID/AST test panel 132. The wells 134 are arranged on the
ID/AST test panels 132 as a two-dimensional array having rows and
columns. The wells 134 are segregated into a ID section 136 and an
AST section 138. In this example, the ID section 136 includes
fifty-one wells 134, and the AST section includes eighty-five wells
134. Each test panel 132 further includes a base 140, a chassis
142, a lid 144, a cellulose acetate pad 146, inoculation ports 148,
and a panel label (not shown) which includes information that
identifies the complete manufacturing history of that test panel
132. Further details of the test panels 132 used with the system
100 are described in copending U.S. patent application Serial No.
09/083,130, referenced above, and in U.S. Pat. No. 5,922,593 to
Livingston, the entire contents of which are incorporated herein by
reference.
[0042] The panel carriers 130 are designed such that they will not
retain improperly seated test panels 132. When the test panels 132
are mounted in the four tiers of the carousel 124, they are
arranged to form substantially circular rows and vertical columns
of wells 134. That is, all the columns of wells 134 in all four
tiers of the carousel 124 should be substantially aligned with each
other in the vertical direction along the entire height of the
carousel 124, while all rows of wells 134 should be substantially
aligned with each other around the entire circumference of the
carousel 124. In this example, panel positions 128 are numbered
zero through twenty-five in each tier of the carousel 124, with
panel position zero being reserved for a normalization panel and
thus not accessible by an operator during normal operation of the
instrument 102.
[0043] The carousel housing portion 106 also includes a drive
module 150 that drives the carousel 124 to rotate in a clockwise or
counter-clockwise manner, as desired, and a plurality of bearings
152 and a spring-loaded pivot 154 which rotatably secure the
carousel 124 in the interior chamber 115 of the carousel housing
portion 106 and facilitate rotation of the carousel 124. Further
details of the carousel 124 and its associated components, as well
as the panel carriers 130 and test panels 132, are described in
copending U.S. patent application Ser. No. 09/083,130, referenced
above.
[0044] As shown in FIGS. 3 and 5, and in the schematic diagram
shown of FIG. 6, the carousel housing portion 106 in this example
further includes a visible light source assembly 156 and an
ultraviolet (UV) light source assembly 158. The visible light
source assembly 156 includes four visible light source modules
156-1 through 156-4 and a supporting tower 160, while the
ultra-violet light source assembly 158 includes ultraviolet light
sources 158-1 and 158-2. The supporting tower 160 aligns one
visible light source module with each tier of the carousel 124 so
that at any given time, one entire column of wells of the ID/AST
test panels 132 in the four tiers of the carousel 124 can be
illuminated by the visible light source modules.
[0045] In this example, each visible light source module 156-1
through 156-4 includes three parallel vertical columns of sixteen
light-emitting diodes (LEDs) each. The first column consists of red
LEDs, the second of green LEDs and the third of blue LEDs. A
holographic diffuser plate 162 is disposed in close proximity to
the ID/AST test panels 132 mounted in the carousel 124. The
holographic diffuser plate 162 diffuses the illumination energy
from each column of LEDs, when the columns are energized. Each
column of LEDs is mounted in the visible light source modules-to
maintain a fixed distance from the diffuser plate 162. Cylindrical
lenses (not shown) may be used to focus the illumination energy
from each column of LEDs onto the vertical well columns of the
ID/AST test panels 132. The illumination axis for each column of
LEDs is made coincident for the red, green and blue illumination.
Thus, each well column sees a uniform stripe of either red, green
or blue illumination, depending upon which column of LEDs is
energized.
[0046] As further shown in FIGS. 3, 5 and 6, an optical measurement
system 164 is disposed approximately within the center of the
carousel 124 such that it is aligned to receive the visible light
transmitted through each well 134 of the ID/AST test panels 132
during excitation by red, green or blue illumination from the
visible light source modules of the visible light source assembly
156. Visible fluorescent radiation is similarly detected from the
wells 134 when the samples in the wells 134 are excited by the
ultraviolet light emitted from the ultraviolet light source
assembly 158. As can be appreciated by one skilled in the art,
excitation filters 166 eliminate unwanted spectral components
present in the light emitted from the ultraviolet light source
assembly 158, and emission filters 168 eliminate unwanted spectral
components that may be present in the output signal before
detection by the optical measurement system 164.
[0047] In this example, the optical measurement system 164 includes
a plurality of CCD detector modules 170-1 through 170-4 and
corresponding lens assemblies 172-1 through 172-4, with one CCD
detector module 170 and one lens assembly 172 being aligned to
receive readings from wells 134 of test panels 132 in a respective
tier of the carousel 124. Accordingly, because the carousel 124
includes four tiers in this example, the optical measurement system
164 includes four CCD detector modules 170-1 through 170-4 and four
corresponding lens assemblies 172-1 through 172-4, with each
detector module/lens assembly pair arranged substantially in
alignment in the vertical direction. The lens assemblies 172-1
through 172-4 focus light from of each panel well column of the
test panels 132 in their respective tiers of the carousel 124 onto
the CCD arrays of the corresponding CCD detector modules 170-1
through 170-4.
[0048] Each CCD detector module 170 can include, for example, a
2048-pixel linear CCD array. The CCD arrays of the CCD detector
modules 170 detect and measure the intensity of light transmitted
through each well 134 of the test panels 132 in the corresponding
tiers of the carousel 124 when the wells 134 are illuminated by the
red, green and blue LEDs. Visible fluorescent light is similarly
detected by the CCD arrays of the CCD detector modules 170 when the
samples in the wells 134 are excited by the ultraviolet light
emitted from the ultraviolet light source assembly 158. Further
details of the structure and operation of the visible light source
assembly 156, ultraviolet light source assembly 158, optical
measurement system 164, and their related components can be found
in copending U.S. patent application Ser. No. 09/083,130,
referenced above.
[0049] As stated above, FIG. 6 is an exemplary schematic diagram
illustrating further components of the measurement instrument 102
described above. As shown, the carousel housing portion 106 and the
controller housing portion 108 are separated by a divider panel 174
which can be, for example, part of the housing 106. The ultraviolet
light sources 158-1 and 158-2 are driven by a lamp driver 176. The
lamp driver 176, visible light source modules 156-1 through 156-4,
CCD detector modules 170-1 through 170-4, an ultraviolet light
source cooling fan 178, and an optical measurement system cooling
fan 180 are coupled to an interconnect board 182. A plurality of
status indicator boards 184, barcode readers 186 which read the
barcodes on the test panels 132, and panel flags and home flag
reader 188, are also coupled to the interconnect board 182. Further
details of the status indicator boards 184, barcode readers 186,
and panel flags and home flag reader 188 can be found in copending
U.S. patent application Ser. No. 09/083,130, referenced above.
[0050] The interconnect board 182 is coupled to an I/O interface
board 190 of the controller module 191 that is present in the
controller housing portion 108. As described in more detail below
and in copending U.S. patent application Ser. No. 09/083,130,
referenced above, the control module 191 includes a controller 192
which controls the visible light source modules 156-1 through
156-4, CCD detector modules 170-1 through 170-4, lamp driver 176,
and all other components associated with performing the well
reading process. The controller 191 further includes an ethernet
194 and an LCD driver 196. The ethernet 194 can be coupled to a
network port 198 to output and input data to and from the
workstation 110 (see FIG. 1), for example. The LCD driver 196 is
coupled to the display panel 116 (see FIG. 1) to display, for
example, results of the well readings, and is further coupled to an
external video connection 197. The controller 192 is coupled to the
computer readable medium drive 120 (see FIG. 1) to output and input
data to and from a computer readable disk, for example.
[0051] In addition, the controller module 191 is coupled to the
computer readable medium drive 120, to the display panel 116 via an
inverter 200, to the keyboard panel 118 via an indicator board 202,
to the barcode reader 121, to an AT keyboard 204 and to a speaker
206. The controller module 191 is further coupled to an auxiliary
serial port 208, a printer port 210, a remote alarm port 212 and an
auxiliary barcode reader port 214 which, along with the network
port 198, are housed in a connector panel 216. In this example, the
barcode reader port 214 is coupled to the barcode reader 122 (see
FIG. 1).
[0052] The controller module 191 is also coupled to a drive and DC
distribution module 218 and a power control and distribution module
220. An ambient temperature sensor 222 and an incubation
temperature sensor 224 sense the temperature inside the interior
chamber 115 and provide signals indicative of the temperature to
the controller 192 of the controller module 191. Furthermore, upper
temperature cut-off sensor 226 provides a signal to the controller
192 via the power control and distribution module 220 indicating
when the temperature of the interior chamber 115 has reached the
maximum temperature. In response, the controller 192 will control
the heater 228 via power control and distribution module 220, and
will control heater blower 230 via drive and distribution module
218, to prevent the temperature in the interior chamber 115 from
further increasing. The controller 192 further controls the door
switch 232 and door solenoid 234 via drive and distribution module
218 to control the latch mechanism 114 of the door 112 (see FIGS. 1
and 2) to either maintain the door 112 in the closed position or
allow the door 112 to be opened. The controller 192 also controls
the drive module 150 to control the rotation of carousel 124 as
described in detail below. Further details of the temperature
controlling operations and carousel rotation operations are set
forth in copending U.S. patent application Ser. No. 09/083,130,
referenced above.
[0053] As further shown in FIG. 6, the controller housing portion
108 includes a transformer 236 and cooling fans 248 that are
coupled to the power control and distribution module 220. Also, a
24 V power supply 240, a 15 V power supply 242 and a 5 V power
supply 244 provide power to the drive and distribution module 218
and power controller module 220, as well as to the lamp driver 176.
These power supplies 240, 242 and 244 are powered from an A.C.
input power that is received by the power control and distribution
module 220 via filter 246 and the main on/off switch 248 of the
system 100.
[0054] The operation of the system 100 will now be described with
reference to FIGS. 1-6, as well as the flow chart and graphs shown
in FIGS. 7A-9. In Step 1000, each test panel 132 is inoculated with
a respective broth-suspended organism (i.e., a sample) before being
placed into a respective panel carrier 130 of the carousel 124. The
separate innocula are added manually to the inoculation ports 148
of the test panels 132, and allowed to flow into the wells 134
of-the test panels 132 as described in copending U.S. patent
application Ser. No. 09/083,130, referenced above. Only one type of
sample is introduced into each respective test panel 132. As
discussed-above, the wells 134 of the test panels 132 include
various types and concentrations of antimicrobial materials, which
affect the growth of the samples, along with indicators that
indicate the presence or absence of sample growth. Also, at least
one of the wells 134 of each test panel 132 is designated as a
growth control well and does not include any antimicrobial
material.
[0055] The inoculated test panels 132 are then inserted into the
respective panel carriers 130 of the carousel 124 in step 1010. The
operator uses the barcode scanner 121 or barcode scanner 122 to
scan the barcode of each test panel 132 as it is being inserted
into a respective panel carrier 130, to thus enter information
pertaining to the sample in the test panel 132, the antimicrobial
materials in the test panel wells 134, and so on, into the system
100. The technician also can enter information pertaining to the
tier level and position in the carousel 124 at which the test panel
132 is inserted via the keyboard 118, for example. Once the test
panels 132 have been loaded into the carousel 124, the door 112 of
the carousel housing portion 106 is closed and latched shut. In
step 1020, the controller 192 controls the carousel 124 to begin
rotating, and controls the heater 228 and heater blower 230 to
begin increasing the temperature of the interior chamber 115 to
incubate the samples in the wells 134. In this example, the
operator can set the carousel 124 to rotate at one revolution per
minute (RPM). However, the rotational speed can be set to any value
as appropriate.
[0056] After a predetermined amount of time has passed, for
example, two hours, the controller 192 controls the system 100 to
begin taking measurements of the wells 134 of the test panels 132
in a manner as described in copending U.S. patent application Ser.
No. 09/083,130, referenced above. In this example, measurements are
taken at 20 minute intervals. Also, as can be appreciated from the
discussion below, the following steps in the flowchart shown in
FIG. 7 are performed for each panel 132, and the manner in which
the processing proceeds for each respective panel 132 is dependent
on the results of the well readings obtained for each respective
panel 132. Also, the operations described in these steps are
controlled by controller 192.
[0057] In step 1030, the first readings of the wells 134 of the
test panels 132 are taken as test readings, to determine whether
the readings pass an initial criteria indicating that the samples
are valid for analysis. The well readings are taken as the carousel
is being rotated. The controller 192 waits until the home flag of
the carousel 124 is detected by the home flag detector 188 before
beginning to take the readings, to insure that the controller 192
can match the readings with the correct well 134 from which the
readings were taken.
[0058] The controller 192 can first control the detector modules
170-1 through 170-4 to perform dark readings, during which neither
the UV light sources 158 nor the visible light sources 156-1
through 156-4 are energized. The controller 192 can then control
the lamp driver 176 to drive the ultraviolet light source assembly
158. The controller 192 in this example waits until the carousel
124 has rotated two revolutions to allow the ultraviolet lights of
the ultraviolet light source assembly 158 to warm up, so that the
light intensity can stabilize, and then controls the detector
modules 170-1 through 170-4 to take an ultraviolet light reading
for an entire revolution of the carousel 124. The controller 192
then controls the lamp driver 176 to turn off the ultraviolet light
sources 158, and processes the readings. As discussed in copending
U.S. patent application Ser. No. 09/083,130 referenced above, the
controller 192 uses the ultraviolet readings to identify the types
of samples in the sample wells 134 of the test panels 132.
[0059] After the above readings have been taken, the visible light
readings are then taken. The controller 192 can then control the
rate of rotation of the carousel 124 to remain the same, or can
increase the rate of rotation of the carousel 124, for example, to
2 RPM, or any other suitable rotation speed, while the visible
light readings are being taken. In one example, the rotation speed
is increased to 2 RPMs, and the red LEDs of the visible light
source assembly 156 (see FIGS. 3, 5 and 6) are activated. The
carousel 124 can be rotated one revolution to allow the red LEDs to
warm up so that light intensity can stabilize, and then "red"
readings can be taken of the wells 134 by the detector modules
170-1 through 170-4 while the carousel 124 rotates the second
revolution.
[0060] Once the red readings have been taken, the red LEDs are
turned off and the green LEDs of the visible light 156 can be
energized. As with the red LEDs, the carousel 124 can be rotated
one revolution to allow the green LEDs to warm up to allow the
light intensity to stabilize. The "green" readings can then be
taken of the wells 124 by the detector modules 170-1 through 170-4
while the carousel 124 is rotated another revolution. After the
green readings have been taken, the green LEDs are turned off. In
this example, the rotation speed of the carousel 124 is then
reduced to 1 RPM, and the blue LEDs of the visible light source
assembly 156 are energized. The carousel 124 is allowed to rotate
for one revolution while the blue LEDs warm up to allow the light
intensity to stabilize. Then, the "blue" readings of the wells 134
are taken by the detector modules 170-1 through 170-4 during the
next revolution of the carousel 124.
[0061] The red, green and blue readings taken for each well 134 of
each test panel 132 are then stored by the controller 192 in a
memory such that each well 134 has a specific red, green and blue
reading for that particular time interval. The process then
continues to step 1040 where the readings for each well 134 are
evaluated to determine whether the further readings that are taken
on a well 134 are to be considered valid.
[0062] In step 1040, the red readings taken of each well 134 are
evaluated to determine whether the wells have been properly filled.
The readings can range from an intensity level of "0" to an
intensity level of "4200" with 0 being zero intensity and 4200
being the maximum intensity reading for a particular color (e.g.,
red). In this example, the process identifies in step 1040 the
wells 134 having a red reading above 2200. For those wells 134
having such a red reading, the processing continues to step 1050
where those wells 134 are failed or, in other words, the system 100
identifies all future readings from those wells 134 as being
invalid. Accordingly, either no further readings of those wells 134
are taken, or any readings that are taken are ignored.
[0063] Furthermore, if a well 134 has been identified as a growth
control well and has a red reading of over 2200, the entire side of
the test panel 132 on which that control well resides is failed.
Also, if that well contains a particular antimicrobial material, no
results are reported by the system 100 for that antimicrobial
material for the particular test panels 132 including the failed
wells.
[0064] Once the red well readings have been evaluated in step 1040
and the appropriate wells 134 have been failed in step 1050, the
processing continues to step 1060 where a panel indicator
determination is made. Specifically, in this step, the wells 134
identified as growth control wells for their respective test panels
132 are evaluated to determine whether the initial state of the
growth indicator present in the samples in the control wells 134 of
their respective test panels 132 are acceptable for evaluating
those test panels 132. In this example, the value of the respective
red reading for each control well is divided by the value of the
respective green reading for each control well. If the result of
the division is less than 0.3692 or greater than 0.6464, controller
192 determines that the initial state of the growth indicator is
unacceptable for the test panel 132 including the control well
providing this result. Accordingly, no results obtained by the well
measurements for that particular test panel 132 are reported. As
stated above, step 1060 is carried out for each test panel 132.
[0065] The processing then continues to step 1070 where the
controller 192 will continue to rotate the carousel 124 and thus,
the carousel housing portion 106 will continue to incubate the
samples in the wells 134. The processing will continue to step 1080
where the system 100 will take the red, green and blue readings of
the wells in a manner similar to that described to above with
regard to step 1030, and as described in copending U.S. patent
application Ser. No. 09/083,130, referenced above. The processing
then proceeds to step 1090 where the controller 192 determines
whether the minimum amount of incubation time has elapsed. The
minimum incubation time at which readings of the wells 134 can
begin to be analyzed to determine MIC values in this example is two
hours. If the minimum incubation time has not elapsed, the
processing returns to step 1070 and the incubation is continued.
However, once the appropriate amount of incubation time has
elapsed, the processing proceeds to step 1100 where the controller
192 will calculate the redox state and turbidity values for each
well.
[0066] The system 100 in this example uses two indicators of
growth, redox and turbidity, to evaluate the susceptibility of the
samples to the antimicrobial materials in the wells 134. The redox
and turbidity values are calculated for each well 134 in each of
the panels based on the red, green and blue readings taken of the
respective wells at the respective 20 minute time intervals as
discussed above. A simultaneous nonlinear algorithmic model was
developed from experimentally obtained redox and turbidity
readings, and this algorithm is used by the controller 192 to
predict the redox state and organism density (turbidity) in each of
the wells 132. The controller 192 can arrange the calculated redox
state and turbidity values for each respective well 132 in graph
form with respect to incubation time. An example of the calculated
redox and turbidity growth curves for E.coli for a single well 132
is shown in FIG. 8.
[0067] As stated above, the redox state of a sample in a well 132
is measured by utilizing the change in red, green and blue readings
that occurs over time as a result of the reduction of a growth
indicator, such as resazurin, by the antimicrobial material in the
well 132. As the resazurin is reduced, the color of the sample in
the well 132 changes from blue to red to clear. This change in
redox is represented numerically as a continuum, with the value "0"
indicating an unreduced growth indicator (blue=resazurin), the
value "0.5" indicating that the indicator is 50% reduced
(red=resorufin), and the value "1.0" indicating that the indicator
has been completely reduced (clear=dihydroresorufin).
[0068] The turbidity is also estimated by using the red, green and
blue reading in an equation similar to the redox calculation. The
initial signal has a value of "0" and a maximum of 2.25 units can
be estimated. The units for turbidity correspond to McFarland units
(1 McFarland=3.times.10.sup.8 cfu/ml).
[0069] An example of the manner in which the actual red, green and
blue readings are used to calculate redox and turbidity values will
now be demonstrated. In this example, the red, green and blue
readings taken of a sample well at the first twenty minute interval
are as follows: red=873, green=956 and blue=2705. The processing
then generates a one-column, four-row input matrix as shown in
Table 1 as follows:
1TABLE 1 Input Matrix Values 1.0000 2705.0000 0.3227 0.3534
[0070] It is noted that the first row in the input matrix is always
padded with the value 1.0000. The value 2705.0000 is equal to the
blue reading, the value 0.3227 is calculated by dividing the red
reading by the blue reading (i.e., red/blue), and the value 0.3534
is calculated by dividing the green reading by the blue reading
(i.e., green/blue). It is also noted that in this example, the blue
reading is clamped at a starting value of 2705 until 36 minutes has
elapsed in the incubation. All points after 36 minutes are
multiplied by the value (2705/(blue signal @ last point before 36
minutes)). The result is then clamped to limits of 558 and 4474.
Furthermore, the value of red/blue is clamped to a minimum of
0.2012329 and a maximum of 1.8936959, while the value of green/blue
is clamped to a minimum of 0.3091655 and a maximum of
1.3084112.
[0071] The processing then multiplies the one-column, four-row
Input Matrix by the four-column, five-row Redox Input Weight Matrix
according to the equation "Input Matrix*Redox Input Weight Matrix"
and known matrix multiplication techniques to arrive at a
one-column, five-row matrix of numbers as discussed below. The
twenty values in the Redox Input Weight Matrix have been calculated
and programmed into the controller 192 based on past empirical data
and observations, and remain constant for all of the readings at
all of the time intervals. An example of the values of the Redox
Input Weight Matrix are shown in the following Table 2:
2TABLE 2 Redox Input Weight Matrix Values -0.673253 0.000710423
-1.623674164 3.340127166 2.445846 -0.000572912 1.4797837
-6.311909249 0.109425 0.005775254 -3.604370752 -0.242927922
1.356753 0.000748697 -2.139010636 -1.067568082 3.88E - 05
0.022713989 3.80317E - 05 2.99302E - 05
[0072] The values of the Intermediate Matrix calculated according
to the above equation "Input Matrix*Redox Input Weight Matrix" are
shown in Table 3 as follows:
3TABLE 3 Intermediate Matrix Values 1.9049 -0.8571 14.4824 2.3143
61.4414
[0073] These values of the Intermediate Matrix, as -well as the
values of the Input Matrix, are used to create a one-column,
nine-row Output Matrix. Specifically, the first row of the Output
Matrix is padded with the value 1.0000, and rows two through six of
the Output Matrix are calculated by taking the antilog value of
each of the above values of the Input Matrix, respectively,
according to the following equation:
antilog value=e.sup.x/(1+e.sup.x)
[0074] with x being the respective value from the matrix. Rows
seven through nine of the Output Matrix are filled with the values
in rows two through four of the Input Matrix. Accordingly, the
values of the Output Matrix are shown in the following Table 4:
4TABLE 4 Output Matrix Values 1.0000 0.8704 0.2980 1.0000 0.9101
1.0000 2705.0000 0.3227 0.3534
[0075] The redox value is then calculated by multiplying the
one-column, nine-row Output Matrix by a nine-column, one-row Redox
Output Weight Matrix according to the following equation and known
matrix multiplication techniques.
Redox Value=Output Matrix*Output Weight Matrix
[0076] In this example, the values of the Redox Output Weight
Matrix are shown in the following Table 5:
5TABLE 5 Redox Output Weight Matrix Values -0.633973 4.167218646
1.721677475 -0.389544272 -2.543872 -0.63391676 1.30610E - 04
0.04646759 -0.842252
[0077] As with the values of the Redox Input Weight Matrix, the
-Redox Output Weight Matrix values have been calculated and
programmed into the controller 192 based on past empirical data and
observations, and remain constant for all of the readings at all of
the time intervals. The Redox Value is thus calculated as
0.23843516. This value is then plotted on the graph as shown in
FIG. 8.
[0078] The turbidity value based on these red, green and blue
readings is calculated in a similar manner. That is, the processing
then generates a one-column, four-row input matrix as shown in
Table 6 as follows:
6TABLE 6 Input Matrix Values 1.0000 2705.0000 0.3227 0.3534
[0079] As with the Input Matrix Values for the redox calculation,
the Input Matrix Values for the turbidity calculations are based on
the actual red, green and blue readings. The first row in the input
matrix is always padded with the value 1.0000. The value 2705.0000
is equal to the blue reading, the value 0.3227 is calculated by
dividing the red reading by the blue reading (i.e., red/blue), and
the value 0.3534 is calculated by dividing the green reading by the
blue reading (i.e., green/blue). It is also noted that in this
example, the blue reading is clamped at a starting value of 2705
until 36 minutes has elapsed in the incubation. All points after 36
minutes are multiplied by the value (2705/(blue signal @ last point
before 36 minutes)). The result is then clamped to limits of 558
and 4474. Furthermore, the value of red/blue is clamped to a
minimum of 0.2012329 and a maximum of 1.8936959, while the value of
green/blue is clamped to a minimum of 0.3091655 and a maximum of
1.3084112.
[0080] The processing then multiplies the one-column,
four-row-Input Matrix by the four-column, five-row Turbidity Input
Weight. Matrix according to the equation "Input Matrix*Turbidity
Input Weight Matrix" and known matrix multiplication techniques to
arrive at a one-column, five-row matrix of numbers as discussed
below. The twenty values in the Turbidity Input Weight Matrix have
been calculated and programmed into the controller 192 based on
past empirical data and observations, and remain constant for all
of the readings at all of the time intervals. An example of the
values of the Turbidity Input Weight Matrix are shown in the
following Table 7:
7TABLE 7 Turbidity Input Weight Matrix Values -2.870675 0.002111599
-0.234543715 0.334025395 -1.306260 0.00202755 0.577175204
-2.717689223 3.477755 0.001837992 -4.028539894 1.455268741
-0.008775 -0.004819911 -0.027006746 -0.01188475 8.842011
0.001408226 -5.393142566 -4.464335919
[0081] The values of the Intermediate Matrix calculated according
to the above equation "Input Matrix*Turbidity Input Weight Matrix"
are shown in Table 8 as follows:
8TABLE 8 Intermediate Matrix Values 2.8836 3.4041 7.6637 -13.0596
9.3329
[0082] These values of the Intermediate Matrix, as well as the
values of the Input Matrix, are used to create a one-column,
nine-row Output Matrix. Specifically, the first row of the Output
Matrix is padded with the value 1.0000, and rows two through six of
the Output Matrix are calculated by taking the antilog value of
each of the above values of the Input Matrix, respectively,
according to the following equation:
antilog value=e.sup.x/(1+e.sup.x)
[0083] with x being the respective value from the matrix. Rows
seven through nine of the Output Matrix are filled with the values
in rows two through four of the Input Matrix. Accordingly, the
values of the Output Matrix are shown in the following Table 9:
9TABLE 9 Output Matrix Values 1.0000 0.9470 0.9678 0.9995 0.0000
0.9999 2705.0000 0.3227 0.3534
[0084] The turbidity value is then calculated by multiplying the
one-column, nine-row Output Matrix by a nine-column, one-row
Turbidity Output Weight Matrix according to the following equation
and known matrix multiplication techniques.
Turbidity Value=Output Matrix*Output Weight Matrix
[0085] In this example, the values of the Turbidity Output Weight
Matrix are shown in the following Table 10:
10TABLE 10 Turbidity Output Weight Matrix Values -0.107225
-2.957877127 2.378329542 1.866207268 0.012793 -1.741858375
1.38488E-04 -0.08976299 0.401581
[0086] As with the values of the Turbidity Input Weight Matrix, the
Turbidity Output Weight Matrix values have been calculated and
programmed into the controller 192 based on past empirical data and
observations, and remain constant for all of the readings at all of
the time intervals. The Turbidity Value is thus calculated as
0.00459741. This value is then plotted on the graph as shown in
FIG. 8.
[0087] The redox and turbidity values are calculated for each well
based on the readings taken for each well at each time interval
(i.e., each twenty minute time interval in this example), and the
values are plotted on a graph as shown in FIG. 8. A local
regression algorithm (LOESS) smoothes the time series data for both
the redox and turbidity values calculated for each well 132 over
the elapsed period of time. The LOESS in this example uses no more
than seven readings for each local regression. In evaluating a time
point, at least one reading is required past the time point being
interpolated. From the LOESS equations any reading at any time
point can be estimated. From the interpolated data a series of
metrics that describe the growth in the well are calculated. All
metrics will be based on the time or growth control values derived
from these smoothed and interpolated points. The metrics are
derived from the basic functions such as absolute value, first
derivative (rate), second derivative (acceleration) and integral
(area under the curve). The metrics are then used to derive a
series of variables that are utilized by the generalized additive
models (GAMs) as described in more detail below. These variables
are a variety of absolutes, maximums and ratios to the growth
control. A total of 27 variables are available to the GAMs, as
listed below in Table 11.
11TABLE 11 Variables Available for GAMs Running Count Abbreviation
Description 1 CONC_LOG drug concentration 2 T_AB turbidity value 3
T_FD turbidity first derivative 4 T_SD turbidity second derivative
5 T_IN turbidity integral 6 T_AB_M turbidity maximum value 7 T_FD_M
turbidity maximum first derivative 8 T_SD_M turbidity maximum
second derivative 9 T_AB_M_R turbidity maximum value/turbidity
maximum value of the growth control 10 T_FD_M_R turbidity maximum
first derivative/ turbidity maximum first derivative of the growth
control 11 T_SD_M_R turbidity maximum second derivative/ turbidity
maximum second derivative of the growth control 12 T_IN_R turbidity
integral/ turbidity integral of the growth control 13 T_FD_T time
at turbidity maximum first derivative minus time at turbidity
maximum first derivative of the growth control 14 T_SD_T time at
turbidity maximum second derivative minus time at turbidity maximum
second derivative of the growth control 15 R_AB redox value 16 R_FD
redox first derivative 17 R_SD redox second derivative 18 R_IN
redox integral 19 R_AB_M redox maximum value 20 R_FD_M redox
maximum first derivative 21 R_SD_M redox maximum second derivative
22 R_AB_M_R redox maximum value/redox maximum value of the growth
control 23 R_FD_M_R redox maximum first derivative/redox maximum
first derivative of the growth control 24 R_SD_M_R redox maximum
second derivative/ redox maximum second derivative of the growth
control 25 R_IN_R redox integral/redox integral of the growth
control 26 R_FD_T time at redox maximum first derivative minus time
at redox maximum first derivative of the growth control 27 R_SD_T
time at redox maximum second derivative minus time at redox maximum
second derivative of the growth control
[0088] The processing then proceeds to step 1110 during which the
calculated redox state for each growth control well in each
respective test panel 132 is analyzed. If the maximum redox value
for the growth control well of a test panel 132 is not above a
desired value which, in this example, is 0.07, the processing
continues to step 1120. In step 1120, the processing determines
whether the elapsed incubation time has reached a certain desired
duration which, in this example, is 16 hours. If the processing
determines in step 1120 that 16 hours of incubation time or less
has elapsed, the processing returns to step 1070 for this panel
132, and the process repeats as discussed above. However, if the
processing determines in step 1110 that more than 16 hours of
incubation time has elapsed for this particular panel 132, the
processing proceeds to step 1130 where the panel 132 is failed as
being inoculated or containing a non-reactive sample, and no test
results are reported for that panel.
[0089] If the processing in step 1110 determines that the maximum
redox state for the growth control well of the test panel 132 are
greater than 0.07, the processing proceeds to step 1140 for that
panel 132. In step 1140, the processing determines whether the
maximum redox state for the growth control well of that panel 132
is greater than a predetermined value which, in this example, is
0.2. If the maximum redox state for the growth control well in that
panel 132 is not greater than 0.2, the processing continues to step
1150 for that panel 132 where it is determined whether the elapsed
incubation time is greater than a predetermined value which, in
this example, is 16 hours. If the elapsed incubation time is less
than or equal to 16 hours, the processing returns to step 1070 for
this panel 132, and repeats as discussed above. However, if the
processing determines in step 1150 that the elapsed incubation time
has exceeded 16 hours, the processing continues to step 1160, where
the test panel 132 is failed as having insufficient sample growth,
and no results are reported for that test panel 132.
[0090] Concerning step 1140 discussed above, if the processing
determines that the maximum redox state for the growth control well
for the panel 132 is indeed greater than 0.2, the processing
continues to step 1170 where the processing evaluates the type of
curve represented by the calculated redox states plotted in graph
form with respect to incubation time as shown, for example, in FIG.
8. Specifically, based on the maximum redox value of the growth
control well of the panel 132, the processing determines whether
the curve representing the redox values for the growth control well
indicates that the sample is a slow or fast growing sample. If the
processing determines in step 1170 that the curve is classified as
a class "zero" curve, the sample is not yet classifiable as a slow
or fast growing sample because a sufficient incubation time has not
elapsed for that sample. Therefore, the processing returns to step
1070 for that panel 132, and-repeats as discussed above. However,
if the processing determines in step 1170 that the curve
classification is other than "zero", the processing continues to
step 1180.
[0091] In step 1180, the processing determines whether the curve
representing the redox states can be classified as a class "one"
curve. If so, the processing continues to step 1190 where the
controller 192 will perform the appropriate GAM on the redox states
and turbidity values measured for each of the wells 134 in the test
panel 132 to determine the MIC values for the particular sample and
the anti-microbial materials contained in the wells 134 of the test
panel 132.
[0092] In step 1200, the probability of sample growth for each well
134 of the test panel 134 is calculated according to the
appropriate GAM once the growth control is above a specified
threshold. The GAMs were developed for each antibiotic by
evaluating a spectrum of species, MIC values and resistance
mechanisms.
[0093] The GAMs are specific for each antibiotic and broad category
of organisms (gram positive/gram negative). Each GAM requires
approximately 5 of the 27 variables previously described above in
Table 11 to predict growth, but can use as many variables as deemed
appropriate. A GAM uses a polynomial equation as shown below to
describe the relationship between each variable included in the
model and the contribution of that variable in predicting growth in
a well. The calculation of the well probability P.sub.k is simply
the sum of the polynomial functions for each variable and an
intercept term. 1 log ( p p - 1 ) = + f 1 ( x 1 ) + + f 1 ( x 1
)
[0094] Each polynomial function in the above equation represents
the function associated with a respective variable chosen from
Table 11. For example, f.sub.1(x.sub.1) can represent the function
for the first derivative of the turbidity curve at a particular
time interval, f.sub.2(x.sub.2) can represent the function for the
second derivative of the turbidity curve at that time interval, and
so on.
[0095] FIG. 9 illustrates a graph showing an example of the
relationship between a variable and its prediction. These
probabilities are then used to determine the MIC and calculate the
confidence value for the reported MIC as follows.
[0096] Once a set of growth probabilities for each well 134 in the
test panel 132 is derived by the GAM, a probability is calculated
for every possible MIC in step 1210. This MIC probability is the
product of the well probabilities with respect to the values
obtained from the GAM. The example below shows the calculation for
obtaining a probability for an MIC of 16 .mu.g for one
antimicrobial material with respect to the sample in the test panel
132. In this example, the raw probability would be 0.525. It is
noted that five wells 134 of the test panel 132 contain different
concentrations of this antimicrobial material, and the redox and
turbidity results for each of these five wells is used by the GAM
to determine the MIC.
12TABLE 12 An Example of an MIC Calculation for Five Wells
Antibiotic 2 .mu.g well 4 .mu.g well 8 .mu.g well 16 .mu.g well 32
.mu.g well Concentration Pattern for Growth Growth Growth No Growth
No Growth MIC = 16 .mu.g Well Probability 0.9 0.9 0.8 0.1 0.1 (p
from the GAM) Calculation p.sub.2 x p.sub.4 x p.sub.8 x 1-p.sub.16
x 1-p.sub.32
[0097] After a raw probability is obtained, the processing proceeds
to step 1220 where a confidence value for the most probable MIC is
calculated. This is simply the raw probability (P) of the MIC value
(k) over the sum of all valid MIC probabilities as shown in the
following equation: 2 MIC Confidence Value = P k n P k
[0098] Once an MIC and the associated confidence value are
calculated, processing proceeds to step 1230 where this information
is evaluated with respect to a threshold. If the threshold is
exceeded, then the processing proceeds to step 1240 where the
system 100 reports the MIC for the particular sample in the test
panel 132 with respect to the particular antimicrobial material in
the group of wells 134 of the test panel 132. The system 100 can
report the MIC on, for example, display screen 116 of FIGS. 1, 2
and 6, and can also control a printer (not shown) to generate a
printed report.
[0099] However, if a low confidence value is obtained, the
processing proceeds to step 1250 where it is determined whether the
incubation protocol of a certain duration (e.g., 16 hours) has
elapsed. If the incubation protocol has not elapsed, the processing
returns to step 1070 where the test panel 132 continues to incubate
and is reevaluated according to the processing discussed above
after each 20 minute reading. On the other hand, if a minimum
threshold is still not met at the end of the incubation protocol,
the processing proceeds to step 1260 during which the system 100
does not report an MIC for that antimicrobial material, but rather,
provides a message suggesting that the user check purity/viability
and repeat the test.
[0100] A more detailed example of MIC probability calculations is
shown in FIG. 10 for four wells having antibiotic concentrations of
1 .mu.g, 2 .mu.g, 4 .mu.g and 8 jig, respectively. As illustrated
in this example, the probability of growth for a well having a 1
.mu.g concentration of antibiotic as calculated by the polynomial
equation discussed above for a set of readings taken at a
particular interval in time is 0.9. Also, the probabilities of
growth for the wells having 2 .mu.g, 4 .mu.g and 8 .mu.g are 0.9,
0.1 and 0.1, respectively. The five different growth possibilities
are then entered into the table as shown, with the value "0"
representing no growth and the value "1" representing growth. That
is, as shown in the first row of the table, the condition in which
no growth occurs (i.e., "0" for each well) is considered, meaning
that the MIC value would be less than the minimum concentration of
1 .mu.g. The second row illustrates the condition in which growth
occurs in the 1 .mu.g well but in no other wells, the third row
illustrates the condition in which growth occurs in the 1 .mu.g
well and in the 2 .mu.g well, but not in the higher concentration
wells, and so on.
[0101] The four growth probabilities are then multiplied for each
row to arrive at the probability of valid growth pattern values on
the right side of the table. It is noted that because the
probabilities of 0.9 or 0.1 at the top of the table represent
probabilities of growth, these values are subtracted from 1 for
conditions of non-growth to provide a value that is used in the
multiplication. Considering the first row, for example, the
probability of growth for the well concentration of 1 .mu.g is
"0.9". However, because no growth occurred in this well, the value
used in the multiplication is "0.1" (i.e., 1-0.9). This is also the
case for the 2 .mu.g concentration well. Also, because the no
growth occurred in the 4 .mu.g and 8 .mu.g wells, the values for
these wells used in the multiplication are each "0.9" (i.e.,
1-0.1). Accordingly, the multiplication values are
0.1*0.1*0.9*0.9=0.0081, which is the probability that this growth
pattern in the first row is valid.
[0102] The above calculations are performed for each row to provide
the values shown in the first column on right side of the table. In
addition, the probabilities of the "local" growth patterns (i.e.,
the shaded wells in the graph) are multiplied to provide the
probabilities of valid local growth patterns. This additional
calculation is used to increase the accuracy of the results. As
indicated, the row having the MIC possibility of "4" (the third
row) provides the highest probabilities.
[0103] Using the MIC confidence value equation indicated above, the
highest local growth pattern probability of 0.729 is divided by the
sum of itself and the local growth pattern probabilities (i.e.,
0.081+0.729+0.09) to arrive at a MIC probability of 0.81 as
indicated. This value is then compared to a predetermined
threshold. If the value exceeds the predetermined threshold, then
the system can report the NEC value of "4" for this sample.
[0104] An example of another table in which wells having antibiotic
concentrations of 0.25 and 0.50 taken into account is shown in FIG.
11. The probabilities, MIC value and MIC probability are calculated
in the same manner as described above.
[0105] It is also noted that prior to reporting the results in step
1240 shown in FIG. 7B, the processing can delay the reporting until
the same MIC value has been determined for a desired number of
consecutive, for example, three time intervals. That is, as can be
appreciated from the graph of FIG. 12 showing redox values for
wells having different antibiotic concentrations, the occurrence of
growth in higher concentration wells can be delayed. For example,
growth in a well having an antibiotic concentration of 1 .mu.g can
occur several hours after growth occurs in a well having an
antibiotic concentration of 0.5 .mu.g. Therefore, the accuracy of
the results can be increased by refraining from reporting an NEC
value until that value has been determined for a desired number of
consecutive intervals, or a desired number of times within a
certain number of consecutive intervals (e.g., 3 times out of 5
consecutive intervals). This delay reduces the possibility that a
lower MIC value will be inadvertently reported.
[0106] It is noted that steps 1200 through 1260 of FIG. 7B are
repeated as appropriate for each respective group of wells 134
containing a respective type of antimicrobial material, so that the
MIC for each antimicrobial material in the test panel 132 can be
reported for the sample.
[0107] Returning now to the discussion of step 1180 of FIG. 7B, if
the processing in step 1180 determines that the curve representing
the redox values for the wells 134 is not a class "one" curve, the
processing proceeds to step 1270 where the processing determines
whether the maximum redox state for the growth control well of the
panel 132 is less than or equal to a particular value which, in
this example is 0.4. If the maximum value of the redox state of the
growth control well is not less than 0.4, it is determined that the
sample is a slow growing sample. Accordingly, the processing
continues to step 1280, where the controller 192 selects the
appropriate GAM to be used to evaluate the redox and turbidity data
for the wells 134 of the test panel. The processing then proceeds
to step 1210 where the MIC values are determined as discussed
above.
[0108] However, if the processing determines in step 1270 that the
maximum redox state for the growth control well of the panel 132 is
less than or equal to 0.4, the processing continues to step 1290
where the elapsed incubation time of the panel 132 is compared to
predetermined value which, in this example, is 8 hours. If the
elapsed incubation time is less than or equal to 8 hours, the
processing returns to step 1070 and continues as discussed above.
However, if the processing is greater than 8 hours, the processing
continues to step 1300 where the controller 192 selects the
appropriate GAM to be used to evaluate the redox and turbidity data
for the wells 134 of the test panel. The processing then proceeds
to step 1210 where the MIC values are determined as discussed
above.
[0109] As mentioned previously, the processing discussed above is
performed for each test panel 132 being rotated by the carousel 124
of FIGS. 2 and 3. Once all of the test panels 132 have been
evaluated, and the MIC values relating to their respective samples
have been reported, the controller 192 of Gi. 6 controls the heater
228 and heater blower 230 to discontinue heating the inner chamber
115. The controller 192 also controls the carousel 124 to stop
rotating, and unlatches the door 112. The technician can then
remove the test panels 132 and, if desired, commence a new series
of tests using new test panels 132.
[0110] Although only one exemplary embodiment of the present
invention has been described in detail above, those skilled in the
art will readily appreciate that many modifications are possible in
the exemplary embodiment without materially departing from the
novel teachings and advantages of this invention. All such
modifications are intended to be included within the scope of the
invention as defined in the following claims.
* * * * *