Transcript WIP: Variations in Cerebral Hemodynamics During Cardiac

Presenter: Douglas S. Pfeil,
Sergio A. Ramirez, Harry L. Graber, LeRone Simpson,
Dimitre Stefanov, Tigran Gevorgyan, Joshua Burak,
Vinay Tak, Wilson Ko, Randall L. Barbour and Daniel C. Lee
SUNY Downstate Medical Center
Disclosures

None
Cardiac Surgery
More than 600,000 cases/year in US
 Tissue perfusion important
 Monitor perfusion in cerebral tissue to
prevent sequelae

Current Monitoring Strategies
Hemodynamic monitoring
 Electrical activity
 Oxygenation status

 Micro-electrodes into white matter (invasive)
 Jugular venous oxygen saturation (invasive)
 NIRS (near infrared spectroscopy)
NIRS

Near infrared light
(760 and 830nm)
 partial absorption by
Hemoglobin (Hb) – no
contrast needed
Each source-detector
(SD) pair is called a
channel
 Distance of each SD
pair determines depth
probed

NIRS monitoring during surgery

Commercially available BUT assumes one area
of brain the same as the next
 Only 2-4 channels (linear)
Our array


30 sources (S), 30 detectors (D)
4 sites with 7or8 S/D at each site
 total 211 channels (can simulate multiple small array
devices)
C
A
D
B
Patients:
Pilot Data
N=6
 Avg. age: 58

Patients

Hypotensive event (13 periods):
 90sec
 mean arterial pressure (MAP) drop > 20mmHg
(below 60mmHg)* followed by recovery
 Patients: possible cerebral vascular disease &
compromised autoregulation

Intra-op controls (24 periods)
Chillon JM, Baumbach GL (1995) Autoregulation of cerebral blood flow. In: Welch KMA, Caplan LR, Reis DJ, Siesjö BK, Weir B (eds) Primer on
cerebrovascular diseases. Academic Press, San Diego, pp 51–54.
Jennings JR (2003) Autoregulation of blood pressure and thought: preliminary results of an application of brain imaging to psychosomatic medicine.
Psychosom Med 65:384–395. [PubMed]
Paulson OB (2002) Blood–brain barrier, brain metabolism and cerebral blood flow. Eur Neuropsychopharm 12:495–501.
Channel-Channel correlations: positional
heterogeneity between channels
 Channel-MAP correlations:

 instrument sensitive to expected changes
 Regional differences

Sensitivity analysis:
 Mimic real time monitoring
 Need large array to properly detect changes
x 10
2
Channel 4
Channel 7
HBoxy
Channel-channel
time series
correlations
1.5
oxy
1
Hb

-4
0.5
0
-0.5
-1
0
0.5
1
1.5
2
2.5
3
Time
4
x 10
Time
Channel 27
Channel 32
-5
8.5
-6
x 10
8
8
r = .829
6
7.5
4
7
2
6.5
0
6
-2
5.5
0.6
0.7
0.8
x 10
0.9
1
1.1
1.2
1.3
1.4
-4
0.6
r = .096
0.7
0.8
0.9
1
1.1
-4
Channel 25
x 10
1.2
1.3
1.4
-4
Channel 25
x 10

Channel-channel correlations from all 4 sites
% of channels
Mean:
SD:

% of channels
0.42
0.42
Mean:
SD:
0.63
0.43
Wide range implies heterogeneity

Channel-MAP correlations
% of channels
Mean:
SD:



% of channels
0.14
0.15
Mean:
SD:
0.72
0.26
Controls: Physiology other than MAP controlling Hb (e.g.:
autoregulation)
Events: High correlation – expected, sensitivity to events
Sig different between control and events (p<0.001)
Average Correlations Across All Time Periods
Correlation
Value 0.7
p < .06
p < .04
event
0.6
0.5
Bars represent
standard error
0.4
0.3
0.2
0.1
A
0
B
C
D
Site
Sites statistically different!
 Regional variability – but what about
channels themselves?

MAP(mmHG)
Hboxy (a.u.)
-5
80
x 10
2
-3
75
1
-3.5
70
65
0
-4
-1
60
-4.5
-2
55
-3
-5
50
45
-4
-5.5
-5
40
-6
-6
35
7500
15

7520
30
7540
45
Time (seconds)
7560
60
7580
75
Some channels show large amplitude,
others do not.
7600
90
-6.5
-7
Sensitivity
(% of channels)
Pt.3
False negative rate:
22%
b)
X: 17.17
Y: 49.29
X: 17.17
Y: 6.73
Alarm Threshold (% of max change)
• ‘False Negative’ - Channels failing to respond: 19% (4-22%)
• Critical Value (50% of array): 22% (11-45%) of the largest
amplitude
• ‘False Positive’ Channels: 6% (0.5-22%)
Results 3 – sensitivity analysis
(amplitude analysis)
•Patient 1 – StO2
channels with the
highest amplitude
difference during
one event period
Results 3 – sensitivity analysis
(amplitude analysis)
•Patient 6 – StO2
channels with the
highest amplitude
difference during
one event period
Results 3 – sensitivity analysis
(amplitude analysis)
•Patient 6 – StO2
channels with the
highest amplitude
difference during
second event
period
Best for any one channel in top 5%: 50% (13/26) of
Hboxy and St02 events
Conclusions

High spatial variance in brain perfusion

Variability degrades reliability of metrics
intended to detect events

Small array oximetry devices are unlikely
to provide reliable representation of
cerebral perfusion
Acknowledgements

This work was supported by:
 NIH: R21NS067278 – Daniel C. Lee
 NIH: R44NS049734 – Randall L. Barbour
 NYS Department of Health
Extra Information
Cognitive Decline
rSo2 desaturation
score50 > 3,000
Yes
No
Yes
47
96
143
Sens.  33%
No
19
78
97
Spec.  80%
66
PPV
 71%
174
NPV
 45%
An X-ray image of NIRS array setup
Subarachnoid
bleeding

After soft tissue dissection CW-NIRS
array attached directly to the cranium
NIRS DOT
imager array
Cranium
Dissected
Soft tissue

Future biometrics
may rely on
tomography
Large monkey
experiments
simulating strokes collaboration with
other groups
Subarachnoid bleeding
Hboxy (at 124 min.) 3 min.
after beginning of
subarachnoid bleeding
Left ICA/MCA
Occlusion/ stroke area
Hbtotal (at 165 min.) 25
min. after Left ICA /MCA
occlusion
• 3D image
reconstruction
matches very
well with MR
image both in
time and space
Subarachnoid
bleeding