Multimodal network connectivity architecture (MOCA) of the brain and its role in the recovery of consciousness in comatose cardiac arrest patients

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Project Summary Cardiovascular disease remains the leading cause of death in the United States. Mortality rates from cardiac arrest range from 60-85%, and of survivors, up to 80% are initially comatose. Once circulation has been reestablished, the extent of brain injury is a key factor for prognostication. Poor neurologic prognosis commonly leads to the withdrawal of life-sustaining therapies (WLST) and subsequent death. According to the 2015 American Heart Association Guidelines, the most reliable strategy for prognostication of poor outcome remains a key knowledge gap in the treatment of post-cardiac arrest survivors. Traditional recommendations for neurologic prognostication have proven unreliable in modern studies of cardiac arrest patients. New techniques with improved accuracy are needed to avoid premature WLST from patients who may ultimately recover with good neurologic outcome. A large subset of patients initially comatose after cardiac arrest will have normal neuroimaging and electrophysiology, only to deteriorate in the subsequent days. This likely represents a therapeutic window prior to the onset progressive ischemia, apoptosis, hypoperfusion and cerebral edema, which most often leads to poor outcome. We hypothesize that multimodal approaches that include clinical, electrophysiology, biochemical and MRI data will improve prognostication of short-term and long-term outcome in initially comatose cardiac arrest patients, helping to identify those most likely to benefit from therapeutic interventions in the future. We propose to perform a prospective observational study in cardiac arrest patients still comatose 24 hours post-arrest, or 24 hours post-rewarming in those treated with targeted temperature management. Our study focuses on patients for whom prognostication is typically most challenging, and who are most likely to benefit from advanced assessment tools. We will systematically evaluate whether these tests can predict short-term neurological recovery and ultimately long-term neurological outcome. Our first aim is to determine whether acute MRI and electrophysiology can identify which patients are most likely to regain arousal. The benefit of using recovery of arousal as a surrogate marker for long-term outcome is that it will be less plagued by a self-fulfilling prophecy bias from WLST. Secondarily, we will investigate whether subacute MRI and EEG assessments are associated with neurological outcomes at discharge, 3, 6 and 12 months.