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Patient: An elderly man resuscitated from out-of-hospital cardiac arrest underwent brain multimodality monitoring and treatment with therapeutic hypothermia.

Monitored Data: Cerebral perfusion pressure (CPP), intracranial pressure (ICP), brain temperature, brain tissue oxygen (PbtO2), cerebral blood flow (CBF), microdialysis data and intra-cortical EEG were collected at Columbia-Presbyterian Medical Center on several systems and then manually time aligned post hoc. The EEG data was processed to form a Spectral Array which highlights frequency changes of the EEG. The Spectral Array was plotted along the same timeline as the other physiological trends to show correlations.

Clinical Scenario:

  • The Spectral Array shows characteristic frequency distribution changes (arrows) that indicate seizure events (verified on raw EEG recording). Multiple sub-clinical seizure events can be noted within the two hour tracing, representing status epilepticus (SE).
  • This patient responded to the increased cerebral metabolic demand (caused by seizures) with correlating increases in CBF, ICP, CPP, and brain temperature; and a decrease in brain tissue oxygen (PbtO2). Although not depicted here, microdialysis data showed a high ratio of lactate to pyruvate levels, suggesting metabolic disturbance.
  • Antiepileptic therapy was instituted, eliminating the seizures and the corresponding changes in ICP and other measurements.

FIGURE 1: Seizure events (arrows) correlate with increases in CBF, ICP, CPP, and brain temperature; and a decrease in brain tissue oxygen (PbtO2) due to increased metabolic demand (caused by seizures).

Seizure events


This case demonstrates a strong correlation between status epilepticus and alterations in other physiologic parameters. Up to 48% of comatose patients may incur non-convulsive seizures that are accompanied by complex pathophysiologic changes largely undetectable by standard clinical examination in the ICU. Monitoring EEG in conjunction with cerebral physiology improves management of non-convulsive seizures and ultimately patient outcomes.1-6 In this case, if EEG had not been recorded, changes in ICP and PbtO2 may have gone unexplained, resulting in incorrect treatment of the patient.

Because this case was completed without the CNS Monitor, providers had to manually correlate EEG and physiologic data. The CNS monitor provides the ability to configure screens for real-time data display and correlation, eliminating the need for post-hoc processing.

FIGURE 2: Sample CNS Monitor display shows metrics relevant to this case, including CPP, ICP, brain temperature (ICT), PbtO2, CBF (Perfusion), and EEG trends.

Relevant metrics

Reference: Ko S, Ortega-Gutierrez S, Choi H, Claassen J, Presciutti M, Schmidt JM, Badjatia N, Lee K, Mayer SA. Status Epilepticus–Induced Hyperemia and Brain Tissue Hypoxia After Cardiac Arrest. Archives of Neurology. 2011;68(10):1323-1326.

  1. Connolly, M., et al., Characterization of the relationship between intracranial pressure and electroencephalographic monitoring in burst-suppressed patients. Neurocrit Care, 2015. 22(2): p. 212-20.
  2. Helbok, R. and J. Claassen, Multimodal invasive monitoring in status epilepticus: What is the evidence it has a place? Epilepsia, 2013. 54(SUPPL. 6): p. 57-60.
  3. Boly, M. and R. Maganti, Monitoring epilepsy in the intensive care unit: current state of facts and potential interest of high density EEG. Brain Inj, 2014. 28(9): p. 1151-5.
  4. Friedman, D., J. Claassen, and L.J. Hirsch, Continuous electroencephalogram monitoring in the intensive care unit. Anesth Analg, 2009. 109(2): p. 506-23.
  5. Abend, N.S., et al., Nonconvulsive seizures are common in critically ill children. Neurology, 2011. 76(12): p. 1071-7.
  6. Claassen, J. and P. Vespa, Electrophysiologic Monitoring in Acute Brain Injury. Neurocrit Care, 2014.


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