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) A D C 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]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

 Channel-channel time series correlations r =.829 Channel 25 Channel 27 r =.096 Channel 25 Channel 32 HB oxy Time

 Channel-channel correlations from all 4 sites Mean:0.42 SD:0.42 Mean:0.63 SD:0.43  Wide range implies heterogeneity % of channels

 Channel-MAP correlations Mean:0.14 SD:0.15 Mean:0.72 SD: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) % of channels

 Sites statistically different!  Regional variability – but what about channels themselves?

 Some channels show large amplitude, others do not Time (seconds) MAP(mmHG)Hb oxy (a.u.)

b) X: Y: X: Y: 6.73 False negative rate: 22% Pt.3 ‘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%) Alarm Threshold (% of max change) Sensitivity (% of channels)

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 Hb oxy and St0 2 events

 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 Conclusions

Acknowledgements  This work was supported by: NIH: R21NS – Daniel C. Lee NIH: R44NS – Randall L. Barbour NYS Department of Health

Extra Information

Yes No Cognitive Decline Yes No rSo 2 desaturation score 50 > 3, Sens.  33% Spec.  80% PPV  71% NPV  45%

An X-ray image of NIRS array setup After soft tissue dissection CW-NIRS array attached directly to the cranium Cranium Dissected Soft tissue NIRS DOT imager array Subarachnoid bleeding  Future biometrics may rely on tomography  Large monkey experiments simulating strokes - collaboration with other groups

Hbtotal (at 165 min.) 25 min. after Left ICA /MCA occlusion Subarachnoid bleeding Left ICA/MCA Occlusion/ stroke area Hboxy (at 124 min.) 3 min. after beginning of subarachnoid bleeding Subarachnoid bleeding 3D image reconstruction matches very well with MR image both in time and space