(Stroke. 1998;29:137-139.)
© 1998 American Heart Association, Inc.
Automated Intraoperative Detection of Doppler Microembolic Signals Using the Bigate Approach
D. Georgiadis, MD;
A. Wenzel;
H. R. Zerkowski, MD;
S. Zierz, MD;
A. Lindner, MD
From the Departments of Neurology and of Cardiothoracic Surgery (H.R.Z.),
Martin-Luther University of Halle-Wittenberg, Halle, Germany.
Correspondence to D Georgiadis, MD, Department of Neurology, University of Halle, Ernst-Grube-Str 40, 06122 Halle, Germany. E-mail dimitrios.georgiadis{at}medizin.uni-halle.de
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Abstract
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Background and Purpose—We undertook
this study to evaluate the performance of an automated
detection software in the detection of Doppler
microembolic signals (MES) during cardiac
surgery.
Methods—Intraoperative monitoring was performed over two
spatially separated vessel segments of each middle cerebral artery in
18 patients undergoing coronary artery bypass surgery (n=16) or
cardiac valve replacement (n=2). All monitoring sessions were saved on
digital audiotape and subsequently played back to the same ultrasound
machine, set up to automatically detect MES by evaluating the temporary
delay in their appearance between the two segments, in the presence of
an experienced examiner. Software sensitivity and specificity in MES
detection were then evaluated, with the results of the human observer
considered the gold standard.
Results—A total of 44 933 high-intensity signals (artifacts and
MES) were evaluated. Overall sensitivity and specificity of the
software, with the human observer considered the gold standard, were
64% and 78.5%, respectively, ranging from 54% to 96% and from 74%
to 90% in individual patients. When the overall results of the
software were compared with those of the human observer, 
was
0.72.
Conclusions—The tested software displayed a satisfactory
specificity. Provided that the sensitivity is further improved, it
could provide a valuable tool in intraoperative monitoring.
Key Words: cardiopulmonary bypass • embolism • ultrasonography, Doppler
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Introduction
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Since the first
description of Doppler MES during cardiac surgery,1
several studies examined their clinical relevance and reported a
relation between the prevalence of perioperative
complications and MES counts.2 3 4 5 Intraoperative MES
detection, however, is a time-consuming procedure, requiring both the
presence of an experienced examiner during monitoring and precise
off-line evaluation of MES counts. Automated MES detection therefore
provides an attractive tool in this setting. A promising detection
technique based on "multirange" monitoring was introduced by Aaslid
in 1994 and has since been evaluated in a number of
studies.6 7 8 9 We examined the applicability of this
technique for automated MES detection during cardiac surgery.
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Subjects and Methods
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Bilateral TCD monitoring was performed in 18 patients (13 men, 5
women, aged 64±2 years) undergoing elective cardiac surgery for
coronary artery bypass grafting (n=16) or cardiac valve
replacement (n=2), with the use of a pulsed ultrasound machine
(Multi-Dop X-4, DWL) with 2-MHz transducers during the entire operative
procedure. Window overlap was set at 60% and sample volume at 5
mm. Power was reduced to 20 to 40 mW/cm2 to ensure adequate
dynamic range (>65 dB), and the detection level for MES was set at 9
dB. We used 64-point fast Fourier transform. Monitoring was performed
over two spatially separated segments of both middle cerebral arteries,
whose distance ranged between 5 and 8 mm. Monitoring sessions were
saved on DAT with an 8-channel recorder (TASCAM DA 88). An
experienced examiner, who recorded the stages of the operative
procedure based on the time of the DAT and readjusted the probe when
necessary, was present during all monitoring sessions.
All tapes were subsequently played back to the same Doppler
machine. The embolus detection software TCD-8, version 8.00 T, was
used. This software initially identifies all signals causing an
intensity increase higher than a preset value (in our application 9 dB)
above background, thus purely using an energy threshold. Background
intensity is thereby evaluated by averaging the intensity of the
spectra following the MES over a period of 10 milliseconds in the
frequency domain. Subsequently, the temporary delay in the appearance
of each high-intensity signal is calculated, based on the particle
velocity measured in the proximal sample volume and the distance
between the two sample volumes. Signals appearing between the two
depths monitored with a temporary delay ranging between 25% and 250%
of the value calculated above are accepted as MES, while remaining
signals are rejected as artifacts. All high-intensity signals
recognized by the software are listed with a subheading displaying
either the signal strength in decibels, if classified as MES, or xx, if
classified as artifacts. The examiner was asked to note the number of
MES and artifacts correctly identified as such by the automated
software, the number of MES incorrectly rejected as artifacts, the
number of MES that the software failed to identify, and the number of
artifacts incorrectly classified as MES. This evaluation was performed
separately for each patient. The operating procedure was divided into
the following stages: (1) chest opening to aortic cannulation, (2)
aortic cannulation and inception of bypass, (3) cardiopulmonary
bypass, and (4) termination of bypass until skin closure; the above
counts were evaluated for each substage.
The human observer was used as the gold standard in MES identification.
The following identification criteria were used: characteristic sound,
intensity increase 
3 dB above background, short duration (<300
milliseconds), and random appearance in the cardiac cycle according to
a recent consensus.10 Bidirectional signals fulfilling the
remaining criteria were not rejected. MES "showers" were not
evaluated because quantification was not feasible in these cases.
The specificity and sensitivity of the applied software were calculated
(number of MES detected/total number of MES and number of artifact
signals rejected/total number of artifact signals, respectively) for
each patient and each operation substage, with the human observer used
as the gold standard. Additionally, Cohen's was evaluated by
comparing the results of the human observer with the results obtained
by the automated software.11 These values range between -1
(complete disagreement) and 1 (complete agreement), whereby 0 reflects
lack of a relation between the evaluations of the two observers. Values
between 0.4 and 0.75 indicate acceptable to good, and those >0.75
indicate excellent agreement.11 Normally distributed data
were expressed as mean±SE and nonnormally as median and 95% CI. The
Mann-Whitney test was used for comparison of nonnormally distributed
data. Significance was declared at the P<.05 level.
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Results
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A total of 44 933 high-intensity transients, consisting of 9411
MES and 35 522 artifact signals, were recorded by the human
observer. A total of 6018 MES were correctly identified as such by the
software, 2498 were classified as artifacts, and 895 were not
identified. Four hundred thirty-seven of the 2498 rejected MES (17.5%)
only appeared in the proximal channel; 27 879 of the 35 522 artifact
signals were correctly rejected. Thus, the overall sensitivity and
specificity of the software were 64% and 78.5%, respectively. A total
of 4481 artifact signals occurred in association with coutering (12.6%
of the total artifact count) and were rejected in 75.5% of cases. The
specific agreement ( value) between the software and the human
observer in the detection of MES and signals was 0.72.
MES were only detected in three patients during stage 1; their counts
(95% CI) in the remaining stages were 44 (58 to 77), 215 (57 to 400),
and 100 (27 to 348) (stages 2, 3, and 4, respectively; total=486 [236
to 794]). No significant differences in software performance
were noted among the operation stages (sensitivity, 65% [58% to
77%]; 66% [63% to 72%]; and 63% [57% to 68%], stages 2, 3,
and 4, respectively; specificity, 79% [76% to 82%]; 88% [76% to
100%]; 87% [81% to 94%]; 85% [80% to 90%], stages 1 to 4,
respectively; all P>.05, Mann-Whitney). Software
performance in the individual patients ranged from 54% to 96%
(sensitivity, 64% [61% to 69%], median and 95% CI) and 74% to
90% (specificity, 80% [77% to 82%], median and 95% CI).
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Discussion
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The advantage of the "bigated" approach is the fact that it is
based on a physical principle, thus being able to equivocally identify
each single signal. Three previous studies evaluated the applicability
of this technique: Smith et al6 examined 138 MES and 170
artifact signals and reported a temporary delay of 11.04 milliseconds
(95% CI, 6.24 to 16.41) for MES and 0.08 milliseconds (95% CI, -0.48
to 0.64) for artifacts. Molloy and Markus7 evaluated the
use of this technique in both an in vitro flow model and in vivo
studies and reported sensitivity values of 75.2% and 92.6% in
prosthetic valve carriers and patients with carotid disease,
respectively, and a specificity of 99% for both patient groups, while
100% accuracy was described in the in vitro model. Georgiadis et
al8 reported similar results in an in vitro model and
98.1% and 98.8% sensitivity and specificity, respectively, in patient
studies. Still, these reports examined the applicability of the
method's principle rather than the possibility of automated embolus
detection with commercially available software based on that method.
Droste et al9 recently evaluated the performance of
an automated detection software on 10 prosthetic valve carriers
and 12 normal control subjects. While the reported specificity and
sensitivity were promising (59.9% and 74.3%, respectively), their
results are weakened both by the patient group they studied, since MES
in patients with prosthetic valves are easier to discriminate
because of their higher intensity,12 and by the low total
number (267) of recorded MES. Additionally, artifact signals in
this as well as in previous studies did not appear spontaneously but
were rather directly caused by the examiner or the instructed control
subject and are not necessarily comparable to the monitoring
situation.
A higher sensitivity and a lower specificity are evident when our
results are compared with those of Droste et al.9 This
discrepancy could be coincidental because of differences in specific
MES characteristics between the examined groups or the use of a more
recent software version in the present study.
When MES identification is solely based on automated software,
identification of high-intensity signals is warranted before they are
further classified as MES or artifacts. Failure to recognize the
intensity increase associated with MES was responsible for failure to
identify 895 MES (9.5%) in this study. The sensitivity of the software
could therefore be improved by reducing the detection threshold. We
nevertheless found that the 9-dB threshold provides the best overall
results in intraoperative monitoring (D.G., unpublished data, 1997),
since its reduction results in an almost continuous registration of
artifact signals, making evaluations such as that performed in the
present study almost impossible. The MES detected in only one
channel (17.5% in our study) is most probably due to the fact that the
sample volume is not covering the whole vessel or due to escape of a
number of microemboli through perforating branches. The assumption that
coutering artifacts cannot be rejected by the software used cannot be
confirmed by our results.
The recorded sensitivity and specificity are lower than the
described values of the neuronal network.13 Still, it must
be taken into account that our evaluation was performed
intraoperatively. This approach is more difficult than in patients with
prosthetic valves or potential native embolic sources because
of a higher prevalence of artifact signals, signal disturbances
caused by probe dislocations, variations in blood flow through
inception and termination of cardiopulmonary bypass, and
finally the high intensity and (at least partially) temporary proximity
of MES. Van Zuilen et al14 recently compared two automated
detection software systems based on spectral analysis and a
neuronal network with human observers and reported sensitivity values
ranging between 44% and 70% for the software and 62% for the
neuronal network, while values ranged between 0.18 and 0.93. The
performance of the TCD 8 software is satisfactory compared with
these results, in particular as a result of the aforementioned
additional difficulties associated with monitoring in an intraoperative
setting.
In conclusion, our results suggest that MES detection with the use of
automated software based on the bigate approach is feasible. Further
improvement of its sensitivity would result in a valuable tool for
intraoperative monitoring.
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Selected Abbreviations and Acronyms
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| CI |
= |
confidence interval |
| DAT |
= |
digital audiotape |
| MES |
= |
microembolic signals |
| TCD |
= |
transcranial Doppler ultrasound |
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Received July 11, 1997;
revision received August 25, 1997;
accepted October 16, 1997.
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References
|
|---|
1.
Padayachee TS, Parsons S, Theobald R, Lindley J,
Gosling RG, Deverall PB. The detection of microemboli in the middle
cerebral artery during cardiopulmonary bypass: a
transcranial Doppler ultrasound investigation using
membrane and bubble oxygenators. Ann Thorac Surg. 1987;44:298–302.[Abstract]
2.
Clark RE, Brillman J, Davis DA, Lovell MR, Price TRP,
Magovern GJ. Microemboli during coronary artery bypass
grafting. J Thorac Cardiovasc Surg. 1995;109:249–258.[Abstract/Free Full Text]
3.
Pugsley W, Klinger L, Paschalis C, Treasure T,
Harrison M, Newman S. The impact of microemboli during
cardiopulmonary bypass on neuropsychological functioning.
Stroke. 1994;25:1393–1399.[Abstract]
4.
Barbut D, Lo YW, Gold JP, Trifiletti RR, Yao F, Hager
DN, Hinton RB, Isom OW. Impact of embolization during coronary
artery bypass grafting on outcome and length of stay. Ann Thorac
Surg. 1997;63:998–1002.[Abstract/Free Full Text]
5.
Harrison MJG, Pugsley W, Newman S, Paschalis C,
Klinger L, Treasure T, Aspey B. Detection of middle cerebral emboli
during coronary artery bypass surgery using
transcranial Doppler sonography. Stroke. 1990;21:1512. Letter.[Free Full Text]
6.
Smith JL, Evans DH, Fan L, Bell PRF, Naylor AR.
Differentiation between emboli and artefacts using dual-gated
transcranial Doppler ultrasound. Ultrasound Med
Biol. 1996;22:1031–1036.[Medline]
[Order article via Infotrieve]
7.
Molloy J, Markus HS. Multigated Doppler ultrasound
in the detection of emboli in a flow model and embolic signals in
patients. Stroke. 1996;27:1548–1552.[Abstract/Free Full Text]
8.
Georgiadis D, Göcke J, König M, Hill M,
Nabavi DG, Stögbauer F, Zunker P, Ringelstein EB. Identification
of Doppler microembolic signals using the
coincidence method. Stroke. 1996;27:683–686.[Abstract/Free Full Text]
9.
Droste DW, Hagedorn G, Nötzhold A, Siemens HJ,
Sievers HH, Kaps M. Bigated transcranial Doppler for
the detection of clinically silent circulating emboli in normal persons
and patients with prosthetic cardiac valves. Stroke. 1997;28:588–592.[Abstract/Free Full Text]
10.
Consensus Committee of the Ninth International Cerebral
Hemodynamics Symposium: Basic identification criteria
of Doppler microembolic signals. Stroke. 1995;26:1123.[Free Full Text]
11.
Fleiss JL. Statistical methods for rates and
proportions. In: Statistical Methods and Proportions. 2nd
ed. New York, NY: John Wiley & Sons, Inc; 1981:212–225.
12.
Grosset DG, Georgiadis D, Kelman AW, Lees KR.
Quantification of ultrasound emboli signals in patients with cardiac
and carotid disease. Stroke. 1993;24:1922–1924.[Abstract/Free Full Text]
13.
Siebler M, Rose G, Sitzer M, Bender A, Steinmetz H.
Real-time identification of cerebral microemboli with ultrasonic
feature detection by a neuronal network. Radiology. 1994;192:739–742.[Abstract/Free Full Text]
14.
van Zuilen EV, Mess WH, Jansen C, van der Tweel I, van
Gijn J, Ackerstaff RGA. Automatic embolus detection compared to human
experts: a Doppler ultrasound study. Stroke. 1996;27:1840–1843.[Abstract/Free Full Text]
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Abstract
Introduction
