(Circulation. 1997;96:3030-3041.)
© 1997 American Heart Association, Inc.
Articles |
From The Thoraxcenter and Erasmus University, Rotterdam, the Netherlands, and the University of Washington School of Medicine & VA Puget Sound Health Care System, Seattle.
Correspondence to Kenneth G. Lehmann, MD, Section of Cardiology (111C), Seattle Veterans Affairs Medical Center, 1660 S Columbian Way, Seattle, WA 98108.
| Abstract |
|---|
|
|
|---|
Methods and Results A series of human thrombus models were
constructed in vitro. Spatial homogeneity was ensured by light and
electron microscopy. Quantitative colorimetric
angioscopic analysis demonstrated excellent measurement
reproducibility (mean difference, 0.07% to 0.17%), unaffected by
illuminating light intensity (coefficient of variation, 0.21% to
3.67%). Colorimetric parameters
C1 and C2 were strongly correlated
(r=.99, P<.0001) with thrombus erythrocyte
concentration. Principal components analysis transformed these
parameters into a single value, the thrombus erythrocyte
index, with little (0.06%) loss of content. Measured and predicted
concentrations were similar (mean difference, 0.16 erythrocytes per 1
ng). Randomly ordered images were also subjected to visual
analysis by three experienced angioscopists, with suboptimal
levels of both intraobserver (mean
=0.63) and interobserver (mean
=0.48) agreement. In addition, visual ranking resulted in a Kendall
rank coefficient of 0.72 to 0.76 versus a perfect 1.00 from
quantitative measurement.
Conclusions Quantitative colorimetric angioscopic analysis provides a new, objective, and reproducible analytic tool for assessing angioscopic images of human thrombus. Even under ideal circumstances, experienced angioscopists do a poor job of assessing color (and therefore composition) of human thrombi. This technique can, for the first time, provide quantitative information of thrombus composition during routine diagnostic imaging.
Key Words: thrombus diagnosis fibrin
| Introduction |
|---|
|
|
|---|
Part of the past controversy regarding intracoronary thrombosis arises from the difficulty of its detection in the living heart. Contrast angiography remains the most widely used technique for characterizing coronary pathology. However, thrombus that projects into the vascular lumen is generally surrounded by contrast medium, resulting in a roentgenographic silhouette that frequently masks its presence. Mural thrombus avoids this problem through its adherence to the vessel wall. Its detection is far from assured, however, because angiography can characterize only the arterial lumen and not the adjacent structures. Finally, even if a filling defect is visualized, angiography cannot reliably distinguish thrombus from other common intracoronary structures such as plaques, dissections, or intimal flaps.
Newer imaging techniques such as intravascular ultrasound have the advantage of producing topographic rather that silhouette representations and can interrogate tissues beneath the luminal border.17 Thrombus, though, possesses an acoustic density that differs only slightly from nearby normal and abnormal tissues, making a positive identification particularly difficult.18 It is also possible to use approaches that specifically search for the presence or absence of thrombus. Indium-labeled platelet imaging represents a good example of a procedure that has been quite successful in identifying thrombus localized to the chambers of the heart.19 20 Attempts to extrapolate these impressive results to the detection of thrombus confined to the coronary vasculature have been disappointing, however, likely because of the small mass of thrombus involved.21
Angioscopy has been shown to represent a useful technique in
the recognition of intracoronary thrombus.7 22 23 24 25 26 27 28 29 30 31
Indeed, thrombus detection may prove to be the most important clinical
capability of this emerging diagnostic procedure.
Angioscopy presents several features that make it uniquely suited
to this role. First, its interrogative capabilities are confined to the
intraluminal space and surrounding endoluminal surface. This is
precisely the area where occlusive coronary thrombi are
generated and located. Second, unlike other techniques, angioscopy can,
in a single image, visualize a major portion of the surface of a
thrombus. This enhances one's ability to distinguish thrombus from
other protruding structures such as atheroma. Third,
angioscopy provides a spatial resolution, measured in our laboratory,
of
50 µm. This compares favorably to resolutions of
200 µm for angiography32 and
150 µm
for intravascular ultrasound.33 Finally, angioscopy
represents the only diagnostic technique in
cardiology capable of providing information on true
color. Because the color of thrombus frequently differs from that of
surrounding structures, better diagnostic specificity is
realized.
In the current study, we investigated whether the composition of a human thrombus could be ascertained through characterization of the light reflected from its surface. This was accomplished using a new tool developed in our laboratory specifically for the quantification of images obtained during angioscopy.
| Methods |
|---|
|
|
|---|
The thrombi were formed in a series of 12-mm-diameter cylindrical polystyrene containers held in a 37°C water bath. By use of a precision micropipette (rated accuracy ±1.0%), appropriate volumes of the plasma mixture and blood were combined to produce 18 different erythrocyte concentrations. The final target concentrations of erythrocytes in the cylinders, relative to the original anticoagulated blood, were 100%, 80%, 60%, 40%, 30%, 25%, 20%, 15%, 12%, 8%, 6%, 4%, 2%, 1%, 0.5%, 0.2%, 0.1%, and 0%. After mixing, each mixture was maintained at 37°C for 45 minutes before clotting to enhance platelet activation. Before clotting, a 700-µL aliquot of each mixture was sent for assessment of the concentration of erythrocytes, leukocytes, and fibrinogen. Clotting was initiated with the addition of 230 µL of 0.2 mol/L calcium chloride, resulting in a final volume of blood mixture of 4530 µL in each cylinder. During clotting, each cylinder was gently agitated at 3-minute intervals to prevent cell settling, and each was capped to prevent the loss of CO2 with a resultant change in pH. All clots were maintained at 37°C for a minimum of 18 hours to allow sufficient time for full clot retraction.
The formed thrombus models were removed, accurately weighed, and submerged in Michaelis buffer (sodium acetate and sodium 5,5-diethylbarbiturate titrated to a pH of 7.42)34 contained in a Petri dish. The serum was used for determining the concentration of remaining formed elements and fibrinogen. Each thrombus model was then photographed using constant lighting conditions, exposure settings, and object distance.
Composition of Thrombus Models
The concentration of erythrocytes, leukocytes, platelets,
and fibrin contained in each thrombus model was computed by use of the
differences in volume and concentration of the blood/plasma mixture
before clotting and the serum after clotting. These values were
computed in absolute terms as a quantity per thrombus and in relative
terms per gram of thrombus material. Spatial homogeneity was assessed
by light microscopy of a 5-µm-thick vertical section cut along a
diameter of each cylindrical thrombus model. Examination of these
sections prepared with Goldner stain permitted assessment of the
relative content of each formed element at the top surface (remote from
container wall), at the bottom and side surfaces (adjacent to container
wall), and at various depths within the thrombus model. The
ultrastructure was assessed with electron microscopy of
glutaraldehyde-fixed sections of each thrombus
model.
Angioscopic Imaging
A custom test setup was designed and constructed for in vitro
imaging of the thrombus models (Fig 1
).
Angioscopic imaging was accomplished with a new 4.5F Baxter ImageCath
coronary angioscope. Before testing, a camera white balance was
performed during imaging of an equal-energy white color standard
(Spectralon, Labsphere); the resulting white image was also
recorded for the derivation of correction factors detailed below.
Two 0.3-mm-diameter steel pins were mounted at the tip of the setup
device just outside the image field. These pins were brought into
contact with the surface of the thrombus model undergoing angioscopic
imaging, thereby maintaining a fixed and reproducible 1.7-mm distance
from angioscope tip to object surface. All imaging was accomplished
with the angioscope tip and thrombus model submerged in a buffer
solution to reduce surface reflection and more closely approximate in
vivo imaging conditions. A flat black background was used to minimize
reflection of any traversing light energy. The thrombi were illuminated
through the angioscope with a 300-W xenon light source (Baxter model
OPTX 300), and the image was captured on an Envision model 2705
single-chip, 1/3in (8.38-mm) CCD video camera with the automatic
shutter control disabled to prevent autoregulation of light level. To
permit subsequent off-line analysis, all images were
recorded on 0.5-in super VHS videotape using the "Y/C" video
signal. Imaging was performed in a fully darkened room with the
angioscope shielded from stray light emanating from the light source
ventilation ports or video monitor screens.
|
All thrombi models were oriented so that imaging was performed on the top surface of the clot, an area not in contact with the container wall during the final stages of clotting. Each thrombus model was imaged four times, once at each of four light intensity settings. The settings used were held constant for all testing and were selected to provide a wide range of light intensities while maintaining the mean luminosity values within the valid working range as previously established in our laboratory.35 To assess the homogeneity of the thrombus model, each thrombus was bisected after completion of surface imaging and a new image obtained from its center.
Image Processing and Analysis System
Tristimulus colorimetric measurements were made
from the images of the thrombus models using the quantitative
colorimetric angioscopic analysis system
developed in our laboratory. Detailed descriptions of this system have
been published previously.35 36 In brief, video images
obtained either directly from the camera or from videotape were
digitally sampled by use of a fast 24-bit (8 bit per channel)
analog-to-digital conversion system. This system permits real-time
(1/25 second) digitization in 16.7 million colors and provides a
spatial resolution of 720x576 for PAL (720x480 for NTSC).
Single-frame images were acquired and stored on a microcomputer in a
640x480x24-bit-deep bit map format. A custom image analysis
program permitted the user to select a rectangular region of interest
intended for quantitative colorimetric
analysis. Red, green, and blue (RGB) color values for each
picture element (pixel) contained within the selected region were
computed. These values were corrected for errors inherent to the
hardware imaging chain by use of three sequentially applied algorithms
(color-dependent offset value, software-derived "white balance,"
and exponential gamma correction). The corrected RGB values were
transformed into two other chromaticity coordinate systems. The first,
known as the HSI color space, allows a color to be completely described
by hue, saturation, and intensity. The second, the C diagram, was
developed in our laboratory specifically for angioscopic
imaging.35 In this system, color that is independent of
light intensity is completely defined by two parameters,
C1 (an intensity-independent red index) and C2
(an intensity-independent green index). These
colorimetric parameters were normalized to
provide a full range of 0 to 1.
All image analyses were performed without knowledge of the composition of the thrombus models. To maximize objectivity, a predefined and reproducible region of interest for quantitative measurement was used for all images.
Visual Interpretation of Thrombus Models
To assess the accuracy of visual discrimination of color between
the thrombus models, three experienced angioscopists were asked to
blindly grade and order the color of all images of the erythrocyte
thrombus models containing a thrombus erythrocyte concentration of 8
cells per 1 ng or less. None of these individuals were involved with
either the creation or imaging of the thrombus models. To accomplish
this task, each expert was presented with 44 images of the 11
thrombus models, each recorded at four different light intensities.
The order of presentation was set randomly by a
computer-generated random number table, and the images were shown
sequentially for 5 seconds with a 5-second pause between images. The
experts were asked to order the images from the greatest to least
concentration of erythrocytes, as well as assign each thrombus model to
one of five ordered color categories: red, whitish-red, red-white,
reddish-white, and white.
Data Analyses
Continuous variables were analyzed by use of the
two-tailed t test. Linear and nonlinear regression
techniques were used to explore the relations between different
colorimetric indexes, as well as in the construction of
a model predictive of thrombus erythrocyte concentration.
Multidimensional data were reduced to a single dimension by principal
components analysis. To assess observer agreement, the
statistic was used to assess the magnitude of differences found both
between and within observers. The accuracy of ranking of angioscopic
images was analyzed using Kendall's rank correlation. Two-way
ANOVA was used to investigate the relation between illuminating light
intensity and visual ranking of thrombus model color. Plus-minus values
represent mean±SD. Mean differences associated with a value of
P<.05 were deemed statistically significant.
| Results |
|---|
|
|
|---|
|
A comparison between planned and observed erythrocyte
parameters is provided in Table 2
. Note that the differences between
measured and predicted values were small, averaging -0.07±0.34% for
hematocrit and -0.42±0.59% for hemoglobin. This confirms the
accuracy of the procedure in achieving the desired erythrocyte
concentrations.
|
The final composition of each thrombus model was computed by use of the
difference in absolute amounts of its component constituents between
the preclot mixture and the postclot serum (Table 3
). In the first 12 thrombus models, a
measurable number of erythrocytes remained in the serum after clotting.
Nearly all the platelets present in the preclotting mixture
were incorporated into the thrombus models, with measurable serum
quantities found in only 3 samples. All measurable quantities of
fibrinogen were consumed in all samples during the clotting
process.
|
Although the volume of preclot mixture was held constant for each
thrombus model, the final weight of the thrombi after clotting differed
significantly (Table 4
), ranging from
0.35 to 1.72 g. To correct for these size differences, the
absolute number of formed elements contained in each thrombus model was
normalized by weight as shown in Table 4
. As expected, the resulting
concentration of erythrocytes differed more than a 100-fold from
thrombus 1 to 18 (9.59 versus 0.08 per 1 ng), with more modest
differences observed in platelet concentrations (0.47 versus 1.74
per 1 ng).
|
Assessment of Measurement Variability
Each thrombus model was imaged during submersion by use of an
angioscope model in common clinical use. The circular image obtained at
the object-to-catheter distance used in this study was
3.4 mm
in diameter. Colorimetric measurement reproducibility
was assessed by analysis of three sequentially acquired images
obtained from each of 11 randomly selected thrombus models. The values
obtained were found to be reproducible during serial imaging, with an
SD of 0.002 for C1 values and 0.001 for C2
values. This corresponded to a mean absolute difference of 0.17±0.13%
and 0.07±0.05% for C1 for C2,
respectively.
It is also important to ensure that the colorimetric values obtained are relatively independent of color intensity, because this latter parameter is highly dependent on both the brightness of the illumination source and the object-to-catheter distance. For this evaluation, serial images were obtained while illumination intensity was varied at the light source over the entire range of clinically useful levels of brightness. Colorimetric analysis of the 72 images obtained (4 per thrombus model) revealed a mean SD below 0.007 for all four colorimetric parameters. Corresponding coefficients of variation (mean±SD) were 1.00±0.79% and 3.67±1.53% for C1 and C2 and were even smaller for hue and saturation (0.21±0.32% and 2.67±3.15%, respectively). Hence, illumination intensity had little impact on the colorimetric values obtained in this study.
Assessment of Color Heterogeneity
Color heterogeneity of the thrombi was assessed in
two ways. First, local heterogeneity was examined with
25 contiguous regions of interest positioned in a 5x5 square matrix
and applied to images obtained from each thrombus model, with the size
of each region measuring
0.32 mm (true scale) in length and
width. Colorimetric analysis of all these 450
regions produced a mean coefficient of variation per thrombus model of
2.71% for C1 and 4.07% for C2. Second, to
examine possible differences between the surface and interior of the
thrombus, each thrombus model was imaged at its center after bisection
as well as at its surface. The colorimetric values
obtained from these two different areas revealed a mean difference of
-0.04±0.06 for C1 and +0.02±0.03 for C2.
These data suggest that the color assessed by quantitative
colorimetric angioscopic analysis is relatively
homogeneous throughout each thrombus model and that
measurement at one site is representative of the
thrombus overall. Histological homogeneity was verified
by microscopy (Figs 2
and 3
).
|
|
Relation of Colorimetric Measurements to
Thrombus Models
The C1 and C2 colorimetric
values obtained from the thrombus models are graphically depicted in
Fig 4
. Starting from thrombus 9,
C1 (the red index) demonstrates a progressive decrease, and
C2 (the green index) demonstrates a progressive increase in
value with decreasing amounts of erythrocytes in the preclot mixture.
These values converge near but not at the white point
(C1=C2=0.333). In contrast, the thrombus models
generated from mixtures containing 100% to 15% blood (thrombus 1
through 8) reveal no apparent change over this range.
|
Fig 5
displays the relation between the
color intensity value and thrombus number. A minor linear increase in
intensity is evident (r=.74) as the number of erythrocytes
in the preclot mixture decreases. Although this trend is statistically
significant (P<.001), the extent of scatter of the
individual values is large enough to preclude useful predictive power
(standard error of the estimate, 13.8). Thus,
colorimetric differentiation of the thrombus models was
well characterized by C1 and C2 values, with
little useful discriminating information provided by intensity.
|
Optimal Characterization of Thrombus Color
The results of the quantitative colorimetric
measurements obtained from the thrombus models can be described using
several different color coordinate systems. Fig 6
shows a polar plot of the
colorimetric values of the thrombus model, with hue
presented in angular values and saturation in radial values.
Saturation varied over most (75.0%) of its possible range and provided
most of the variability that distinguished one thrombus model from
another.
|
The C1/C2 chromaticity diagram provides an
alternative descriptive format (Fig 7
).
With this triangular coordinate system, red colors are located in the
lower right corner, green in the upper left corner, and blue in the
lower left corner. The thrombus models in the current study range in
color from a strong red (thrombus 1) to a white with a slight cyan-blue
tint (thrombus 18).
|
Creation of a Predictive Model
It is important to note that in Fig 7
the observed values occupy a
nearly linear (r=.998) distribution. This pattern permitted
the reduction of the two-dimensional data shown in this figure into a
single dimension, thereby permitting the differentiation of the thrombi
using a more convenient single parameter. Principal
components analysis was used to achieve this transformation.
This process was accomplished with little loss of predictive accuracy,
because the first principal component in this model by itself accounted
for 99.94% of the total observed variance. The resulting model was
weighed more heavily toward C1 than C2, with
linear coefficients of .907 and -.422, respectively.
A final goal was the transformation of this
colorimetry-derived parameter into a more
direct and clinically relevant descriptor of thrombus composition such
as the number of erythrocytes contained within a nanogram of thrombus
material. This allowed the creation of a final model that predicted the
concentration of erythrocytes in a given thrombus based solely on
measured C1 and C2 values. The final predictive
equation is provided below:
![]() |
|
Comparison of Quantitative and Visual Categorization and
Ranking
statistics were used to assess the magnitude of observer
agreement in the 44 randomly ordered images. Using a five-point ordinal
color scale, the assessment of intraobserver variability revealed
values ranging from 0.57 to 0.73, with a mean of 0.63. Analogous
values for interobserver variability were somewhat lower, with a mean
of 0.48 (range, 0.34 to 0.70). None of the individual
values
obtained fell into a range generally considered to indicate good
observer agreement (ie,
>0.75).
In addition to comparing color categories, the sequentially displayed images were ranked, allowing comparisons not only between different observers but also between observers and the quantitative analysis system. Each angioscopist was asked to rank the 44 randomly presented images from the highest to lowest concentration of erythrocytes on the basis of color. A second reading of the entire set of images immediately followed the first. These same images were also subjected to quantitative colorimetric analysis, during which they were ranked on the basis of computed thrombus erythrocyte index values. Calculation of the Kendall rank correlation coefficient permitted assessment of the accuracy of the ranking using the measured thrombus erythrocyte concentration as the standard for comparison. A Kendall coefficient of 1.00 indicates a completely accurate ranking, whereas a value of 0.00 is anticipated for a ranking done solely by chance. In this study, the coefficients obtained from the first rankings of the three trained observers were 0.68, 0.74, and 0.76 (mean, 0.72). The values for the second reading were each slightly better (0.72, 0.75, and 0.81 with a mean of 0.76), suggesting the possibility of a training effect. Compared with these scores, the quantitative colorimetric angioscopic analysis system produced a Kendall rank correlation coefficient of 1.00, indicating a completely accurate ranking of all 44 angioscopic images.
Because each thrombus model was imaged with four different light intensities, it was possible to ascertain whether light intensity itself systematically influences the visual interpretation or quantitative measurement of thrombus color and composition. Similar mean thrombus rankings at each intensity would be expected if visual interpretation were unaffected. However, the mean visual rankings decreased progressively (24.6 to 23.6 to 22.7 to 21.6) as the light intensity decreased from its highest to its lowest value (P=.007). This trend was seen individually for all three observers as well. This finding suggests that trained observers tend to rate the erythrocyte concentration as erroneously high in images that are dimly lit and erroneously low in images that are brightly lit. This pattern of error was not observed with the quantitative colorimetric analysis system, which reported values uninfluenced by light intensity.
| Discussion |
|---|
|
|
|---|
In addition to its diagnostic importance, knowledge of thrombus composition has therapeutic implications. Jang et al38 have reported on the differential sensitivity of erythrocyte-rich and platelet-rich thrombi to lysis using tissue-type plasminogen activator. This difference may account for the apparent paradox of a clearly favorable response to attempted thrombolysis seen in acute myocardial infarction39 40 and the repeatedly poor response observed in unstable angina,12 41 despite the fact that both syndromes are presumably initiated by coronary thrombus formation. Foreknowledge of the relative sensitivity to drug-induced lysis of a given thrombus would be extremely useful to a cardiac interventionalist. A procedure complicated by acute thrombosis could then be most appropriately treated with intracoronary lytic agents or further mechanical intervention.
Interpretation of Thrombus Color
Despite the potentially important role of color in enhancing the
diagnostic abilities of angioscopy, two major practical
problems exist that could greatly limit its substantial theoretic
utility. First, the color of an object as viewed on a video screen can
differ markedly from its true color. The light source, the angioscopic
imaging bundle, the white balance and gamma correction algorithms used
by the camera, the format of video modulation, the linearity of the
recording device, and the manual settings of the monitor color
controls can all induce significant errors in color
accuracy.36 Unfortunately, the impetus driving the
development of video methodology and standards used in angioscopy has
not been scientific concerns but the consumer market in which
physically accurate color rendition may not be important or even
desirable. The second practical problem involves the interpretation of
color. Because of the complexities and individualities of color
perception, each observer interprets and describes color in unique
terms. This has led to many creative but imprecise angioscopic
descriptions of thrombus color reported in the literature such as an
appearance consistent with "tomato puree."42
The magnitude of this limitation was recently highlighted by the
European Working Group on Coronary Angioscopy.43
This group studied observer variability in the classification of
findings derived from 30 clinical angioscopic procedures. Despite the
elimination of color rendition errors discussed above, the
values
for chance corrected intraobserver and interobserver color
variabilities were among the lowest recorded for any category (
range, 0.29 to 0.82 and -0.04 to 0.52, respectively), including some
interobserver values that were worse than predicted by chance alone.
When the equipment-related color distortions occurring at multiple
levels of the imaging chain are combined with the marked subjectivity
of color perception, the visual interpretation of angioscopic color as
currently practiced would appear to have limited clinical utility.
These limitations provided the impetus for the creation of a quantitative approach to color measurement. The precision of the quantitative colorimetric angioscopic analysis system developed in our laboratory has been validated previously.35 36
Thrombus Models
This study demonstrates that human thrombus models can be
constructed in vitro to provide a cellular composition varying over a
wide range in a predictable manner. These models appear to display a
high degree of spatial homogeneity, as gauged by both
histological evaluation and surface/interior color
measurement. Sequential imaging and quantitative
colorimetric measurement of the models provided
repeatable values, with all absolute differences averaging <0.2%.
The C1 and C2 colorimetric
values obtained were nearly independent of illumination intensity (SD
<0.007). This observation is important if this technique is to be
applied to clinical angioscopy. Illumination intensity is determined by
the interaction of power output of the light source and the square of
the object-to-catheter-tip distance. The former variable can be
controlled. However, object distance during in vivo
intraarterial imaging is impossible to fix and difficult to
measure. Structures viewed at a distance visually appear to have a
darker color than when viewed near the angioscope tip. Quantitative
colorimetric analysis avoids this pitfall in
color assessment. Unlike clinical imaging, our in vitro setup allowed
us to set the imaging distance and hence accurately measure the color
intensity of our thrombus models. As Fig 5
shows, little discriminant
information is provided by the accurate knowledge of intensity value;
its exclusion from the final computational model is therefore of little
consequence.
Colorimetry and Thrombus Composition
The relation of colorimetric values and thrombus
model number is graphically depicted in Fig 4
. The plateau effect of
C1 and C2 found at higher preclot
concentrations of erythrocytes would at first glance appear to limit
the usefulness of the technique in distinguishing between
erythrocyte-rich thrombi. The explanation for this observation, though,
appears to lie with differences in size of the formed thrombus models.
Although each model was constructed from equal preclot volumes, Fig 9
shows that the final clot mass
generally decreased with increasing thrombus number. This phenomenon
likely resulted from the relative effectiveness of platelet-induced
clot retraction. In thrombi containing a large concentration of
erythrocytes, the ability of platelets to coalesce and extrude
entrapped serum could be hampered by the presence of the bulky cells,
resulting in a larger thrombus. To correct for this effect,
colorimetric values were compared with the number of
erythrocytes contained within each nanogram of formed thrombus (Fig 10
). Not only was a significant
curvilinear relation achieved (r>.99), but no plateau was
observed. These findings support the utility of quantitative
colorimetric analysis in distinguishing thrombi
over the entire achievable range of erythrocyte thrombus
concentrations.
|
|
Fig 10
also includes the outlier, thrombus model 14. This thrombus
retained a disproportionate volume of serum during its formation (Fig 9
). Although the resulting measured C1 value was 22% lower
than expected from the relation shown in Fig 4
, this apparent error was
completely eliminated by the use of thrombus erythrocyte concentration
as the independent variable.
The colorimetric values obtained were also examined for thrombus erythrocyte content, thrombus hemoglobin content, thrombus hemoglobin concentration, and erythrocyte/platelet ratio. All these relations proved inferior to the thrombus erythrocyte concentration in the strength of their correlation to measured colorimetric values.
Interrelations of Colorimetric Parameters
A nearly linear relationship between C1 and
C2 is evident in Fig 7
. This finding is most commonly noted
in the field of colorimetry when two pure colors are
mixed in progressive proportions. This physical concept appears quite
analogous to our current model involving progressive erythrocyte
concentrations.
In addition to its physical implications, the observed linear relationship in C1 and C2 provides the opportunity of simplifying the predictive model and enhancing clinical applicability. In analogy to the three types of cones in the human retina, tristimulus colorimetry can completely describe any color with the use of three variables, such as RGB or HSI. We have gone one step further in the C1/C2 chromaticity scale by eliminating the contribution of intensity in the description of color, a step that has few practical consequences in angioscopy because of the marked dependence of this parameter on illumination brightness. A two-parameter system of angioscopic color description is still awkward, though, because it requires the use of two numeric values for every color measurement, making comparisons particularly difficult. When quantitative colorimetric angioscopic analysis is used in the study of thrombus, our data suggest that transformation from a two-dimensional to a single-dimensional description of color using principal components analysis is possible with minimal (0.06%) loss of content. Thrombus can therefore be accurately characterized with a single parameter, the thrombus erythrocyte index.
Potential Limitations
It is important to note several possible limitations of our
investigation. First, all the thrombus models were created under static
conditions. In contrast, a sizable proportion of arterial
thrombi form in vivo in continuity with flowing blood. This milieu
frequently leads to thrombi composed of laminations of relatively
erythrocyte-rich and platelet-rich material.44
Although dynamic models are available for the formation of layered
thrombi, they were not used in the current investigation because the
resulting heterogeneity of composition and color would
preclude an accurate comparison of these two
parameters.
Second, because of the desire to carefully control thrombus formation and composition, testing was not extended to other potential in vitro and in vivo thrombus models. Though unproven, extrapolation of our results to other types of thrombus appears rational, because colorimetry relies on physical rather than biochemical properties of the interrogated material.
Finally, although the focus of the current study was the relative contribution of erythrocytes, thrombus color may be potentially influenced by the relative amounts of platelets and fibrin as well. This issue is currently being explored in depth in our laboratory.
Clinical Implications
With increasing recognition of the importance of thrombus in heart
disease comes the need for its improved identification and
quantification. Although modern diagnostic techniques may
be marginally useful in detecting thrombus, they cannot provide
information on composition. Knowledge of composition may prove to be
especially useful in assessing likely outcomes or establishing optimal
treatment.
Our system of quantitative colorimetric angioscopic analysis provides an accurate and reproducible method of determining the concentration of erythrocytes in these thrombus models using a single derived colorimetric parameter. It is clearly superior to the eye of a trained observer in this task and avoids the variability in visual perception created by differing illuminating light intensities. This relatively inexpensive tool is available on-line for rapid analysis within seconds and can, for the first time, provide quantitative information during routine angioscopic imaging.
| Acknowledgments |
|---|
Received December 18, 1996; revision received June 19, 1997; accepted June 26, 1997.
| References |
|---|
|
|
|---|
2. Richardson PD, Davies MJ, Born GVR. Influence of plaque configuration and stress distribution on fissuring of coronary atherosclerotic plaques. Lancet. 1989;2:941-944.[Medline] [Order article via Infotrieve]
3. DeWood MA, Spores J, Notske R, Mouser LT, Burroughs R, Golden MS, Lang HT. Prevalence of total coronary occlusion during the early hours of transmural myocardial infarction. N Engl J Med. 1980;303:897-902.[Abstract]
4.
Falk E. Unstable angina with fatal outcome: dynamic
coronary thrombosis leading to infarction and/or sudden death.
Circulation. 1985;71:699-708.
5. Ambrose JA, Winters SL, Arora RR, Eng A, Riccio A, Gorlin R, Fuster V. Angiographic evolution of coronary artery morphology in unstable angina. J Am Coll Cardiol. 1986;7:472-478.[Abstract]
6. Mandelkorn JB, Wolf NM, Surender S, Schechter JA, Kersh RI, Rodgers DM, Workman MB, Bentivoglio LG, LaPorte SM, Meister SG. Intracoronary thrombus in non-transmural myocardial infarction and in unstable angina. Am J Cardiol. 1983;52:1-6.[Medline] [Order article via Infotrieve]
7. Sherman CT, Litvack F, Grundfest W, Lee M, Hickey A, Chaux A, Kass R, Blanche C, Matloff J, Morgenstern L, Ganz W, Swan HJ, Forrester J. Coronary angioscopy in patients with unstable angina pectoris. N Engl J Med. 1986;315:913-919.[Abstract]
8. Vetrovec GW, Cowley MJ, Overton H, Richardson DW. Intracoronary thrombus in syndromes of unstable myocardial ischemia. Am Heart J. 1981;102:1202-1208.[Medline] [Order article via Infotrieve]
9. Cairns JA, Gent M, Singer J, Finnie KJ, Froggatt GM, Holder DA, Jablonsky G, Kostuk WJ, Melendez LJ, Myers MG, Sackett DL, Sealey BJ, Tanser PH. Aspirin, sulginpyrazone, or both in unstable angina. N Engl J Med. 1985;313:1369-1375.[Abstract]
10. Doherty JE, Schnaper HW, LeWinter MM, Linares E, Pouget JM, Sabharwal SC, Cheslter E, DeMots H. Protective effects of aspirin against acute myocardial infarction and death in men with unstable angina. N Engl J Med. 1983;309:396-403.[Abstract]
11.
Fuchs J, Cannon CP, for the TIMI 7 Investigators. Hirulog in
the treatment of unstable angina. Circulation. 1995;92:727-733.
12.
The TIMI IIIB Investigators. Effects of tissue
plasminogen activator and a comparison of early
invasive and conservative strategies in unstable angina and non-q-wave
myocardial infarction: results of the TIMI IIIB trial.
Circulation. 1994;89:1545-1556.
13. Theroux P, Ouimet H, McCans J, Latour JG, Joly G, Levy G, Pelletier E, Juneau M, Stasiak J, deGuise P, Pelletier GB, Rinzler D, Waters DD. Aspirin, heparin or both to treat unstable angina. N Engl J Med. 1988;319:1105-1111.[Abstract]
14. Neri Serneri GG, Gensini GF, Poggesi L, Trotta F, Modesti PA, Boddi M, Ieri A, Margheri M, Casolo GC, Bini M, Rostagno C, Carnovali M, Abatte R. Effect of heparin, aspirin, or alteplase in reduction of myocardial ischemia in refractory unstable angina. Lancet. 1990;335:615-618.[Medline] [Order article via Infotrieve]
15.
Theroux P, Waters D, Qiu S, McCans J, deGuise P, Juneau M.
Aspirin versus heparin to prevent myocardial infarction during the
acute phase of unstable angina. Circulation. 1993;88:2045-2048.
16.
Fuster V, Badimon L, Cohen M, Ambrose JA, Badimon JJ, Chesebro
J. Insights into the pathogenesis of acute ischemic
syndromes. Circulation. 1988;77:1213-1220.
17. Kimura BJ, Bhargava V, DeMaria AN. Value and limitations of intravascular ultrasound imaging in characterizing coronary atherosclerotic plaque. Am Heart J. 1995;130:386-396.[Medline] [Order article via Infotrieve]
18. Frimerman A, Miller HI, Hallman M, Laniado S, Keren G. Intravascular ultrasound characterization of thrombi of different composition. Am J Cardiol. 1994;73:1053-1057.[Medline] [Order article via Infotrieve]
19.
Stratton JR, Ritchie JL. Indium-111 platelet imaging of
left ventricular thrombi: predictive value for systemic
emboli. Circulation. 1990;81:1182-1189.
20. Ezekowitz MD, Burrow RD, Heath PW, Streitz T, Smith EO, Parker DE. Diagnostic accuracy of indium-111 platelet scintigraphy in identifying left ventricular thrombi. Am J Cardiol. 1983;51:1712-1716.[Medline] [Order article via Infotrieve]
21. Stratton JR. Thrombosis imaging with indium-111 labeled platelets. In: Schelbert HR, Skorton PJ, Wolf PJ, eds. Cardiac Imaging: Principles and Practice: A Companion to Braunwald's Heart Disease Second Edition. Philadelphia, Pa: WB Saunders Co; 1996.
22. Tabata H, Mizuno K, Arakawa K, Satomura K, Shibuya T, Kurita A, Nakamura H. Angioscopic identification of coronary thrombus in patients with postinfarction angina. J Am Coll Cardiol. 1995;25:1282-1285.[Abstract]
23. Mizuno K, Satomura K, Miyamoto A, Arakawa K, Shibuya T, Arai T, Kurita A, Nakamura H, Ambrose JA. Angioscopic evaluation of coronary-artery thrombi in acute coronary syndromes. N Engl J Med. 1992;326:287-291.[Abstract]
24. Teirstein PS, Schatz RA, DeNardo SJ, Jensen EE, Johnson AD. Angioscopic versus angiographic detection of thrombus during coronary interventional procedures. Am J Cardiol. 1995;75:1083-1087.[Medline] [Order article via Infotrieve]
25. den Heijer P, Foley DP, Escaned J, Hillege HL, van Dijk RB, Serruys PW, Lie KI. Angioscopic versus angiographic detection of intimal dissection and intracoronary thrombus. J Am Coll Cardiol. 1994;24:649-654.[Abstract]
26. White CJ, Ramee SR, Collins TJ, Jain SP, Escobar A. Coronary angioscopy of abrupt occlusion after angioplasty. J Am Coll Cardiol. 1995;25:1681-1684.[Abstract]
27. Mizuno K, Miyamoto A, Satomura K, Kurita A, Arai T, Sakurada M, Yanagida S, Nakamura H. Angioscopic coronary macromorphology in patients with acute coronary disorders. Lancet. 1991;337:809-812.[Medline] [Order article via Infotrieve]
28.
White CJ, Ramee SR, Collins TJ, Escobar A, Karsan A, Shaw D,
Jain SP, Bass TA, Heuser RR, Teirstein PS, Bonan R, Walter PD, Smalling
RW. Coronary thrombi increase PTCA risk: angioscopy as a
clinical tool. Circulation. 1996;93:253-258.
29. Feld S, Ganim M, Carell ES, Kjellgren O, Kirkeeide RL, Vaughn WK, Kelly R, McGhie AI, Kramer N, Loyd D, Anderson HV, Schroth G, Smalling RW. Comparison of angioscopy, intravascular ultrasound imaging and quantitative coronary angiography in predicting clinical outcome after coronary intervention in high risk patients. J Am Coll Cardiol. 1996;28:97-105.[Abstract]
30.
Bauters C, Lablanche JM, McFadden EP, Hamon M, Bertrand ME.
Relation of coronary angioscopic findings at coronary
angioplasty to angiographic restenosis. Circulation. 1995;92:2473-2479.
31.
de Feyter PJ, Ozaki Y, Baptista J, Escaned J, Di Mario C, de
Jaegere PPT, Serruys PW, Roelandt JR. Ischemia-related lesion
characteristics on patients with stable or unstable angina: a study
with intracoronary angioscopy and ultrasound.
Circulation. 1995;92:1408-1413.
32. Nissen SE. Radiographic principles in cardiac catheterization. In: Roubin GS, Califf RM, O'Neill W, Phillips H, Stack R, eds. Interventional Cardiac Catheterization: Principles and Practice. New York, NY: Churchill Livingstone, Inc; 1993:409-425.
33. Benkeser PJ, Churchwell AL, Lee C, Abouelnasr DM. Resolution limitations in intravascular ultrasound imaging. J Am Soc Echocardiogr. 1993;6:158-165.[Medline] [Order article via Infotrieve]
34. Michaelis L. Der acetat-veronal puffer. Biochemische Zeitschrift. 1931;234:139-141.
35. Oomen JA, Slager CJ, Lehmann KG, Schuurbiers JC, Serruys PW. Color quantification in angioscopic video images. Med Prog Technol. 1995;21:39-46.[Medline] [Order article via Infotrieve]
36. Lehmann KG, Oomen JA, Slager CJ, deFeyter PJ, Serruys PW. Chromatic distortion during angioscopy:assessment and correction by quantitative colorimetric angioscopic analysis. In press.
37. Cotran RS, V Kumar, SL Robbins. Robbins' Pathologic Basis of Disease. Philadelphia, Pa: WB Saunders Co; 1989:99-120.
38.
Jang IK, Gold HK, Ziskind AA, Fallon JT, Holt RE, Leinbach RC,
May JW, Collen D. Differential sensitivity of erythrocyte-rich and
platelet-rich arterial thrombi to lysis with
recombinant tissue-type plasminogen activator:
a possible explanation for resistance to coronary
thrombolysis. Circulation. 1989;79:920-928.
39. Gruppo Intaliana per lo Studio della Streptochinasi nell Infarto Miocardico (GISSI). Effectiveness of intravenous thrombolytic treatment in acute myocardial infarction. Lancet. 1986;1:349-360.
40. ISIS-2 (Second International Study of Infarct Survival) Collaborative Group. Randomized trial of intravenous streptokinase, oral aspirin, both, or neither among 17,187 cases of suspected acute myocardial infarction: ISIS-2. Lancet. 1988;2:349-360.[Medline] [Order article via Infotrieve]
41.
Waters D, Lam JYT. Is thrombolytic therapy
striking out in unstable angina? Circulation. 1992;86:1642-1644.
42. Inoue K, Kuwaki K, Ochiai K, Ueda K, Takano E, Minato H. Percutaneous transluminal coronary angioscopy as the guiding therapy for intracoronary thrombolysis and angioplasty. In: Vogel JHK, King SBI, eds. Interventional Cardiology: Future Directions. St Louis, Mo: CV Mosby Co; 1989:1-11.
43.
den Heijer P, Foley DP, Hillege HL, Lablanche JM, van Dijk RB,
Franzen D, Morice MC, Serra A, de Scheerder IK, Serruys PW, Lie KI. The
"Ermenonville' classification of observations at coronary
angioscopy: evaluation of intra- and inter-observer agreement, European
Working Group on Coronary Angioscopy. Eur Heart
J. 1994;15:815-822.
44. Ritchie AC. Boyd's Textbook of Pathology. Philadelphia, Pa: Lea and Febiger; 1990:117-132.
This article has been cited by other articles:
![]() |
M. Naghavi, P. Libby, E. Falk, S. W. Casscells, S. Litovsky, J. Rumberger, J. J. Badimon, C. Stefanadis, P. Moreno, G. Pasterkamp, et al. From Vulnerable Plaque to Vulnerable Patient: A Call for New Definitions and Risk Assessment Strategies: Part I Circulation, October 7, 2003; 108(14): 1664 - 1672. [Abstract] [Full Text] [PDF] |
||||
![]() |
W. B. Batchelor, K. W. Mahaffey, P. B. Berger, E. Deutsch, S. Meier, V. Hasselblad, E. T. Fry, P. S. Teirstein, A. M. Ross, C. A. Binanay, et al. A randomized, placebo-controlled trial of enoxaparin after high-risk coronary stenting: the ATLAST trial J. Am. Coll. Cardiol., November 15, 2001; 38(6): 1608 - 1613. [Abstract] [Full Text] [PDF] |
||||
![]() |
E. J. Topol and P. W. Serruys Frontiers in Interventional Cardiology Circulation, October 27, 1998; 98(17): 1802 - 1820. [Full Text] [PDF] |
||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Circulation Home | Subscriptions | Archives | Feedback | Authors | Help | AHA Journals Home | Search Copyright © 1997 American Heart Association, Inc. All rights reserved. Unauthorized use prohibited. |