Back

Cerebral perfusion and metabolism coupling during a critical time window provides rapid assessment of cardiac arrest severity and prognosis in a preclinical model

Wilson, R. H.; Crouzet, C.; Torabzadeh, M.; Bazrafkan, A.; Maki, N.; Alcocer, J.; Tromberg, B. J.; Choi, B.; Akbari, Y.

2019-09-30 neuroscience
10.1101/785972 bioRxiv
Show abstract

Improved quantitative understanding of the dynamic relationship among cerebral blood flow, oxygen consumption, and electrical activity is important to clinicians treating acute brain injury. Such knowledge would elucidate the neurovascular response to ischemia, helping to potentially guide treatment. Using a multimodal optical imaging platform and a clinically-relevant rat model of cardiac arrest (CA) and cardiopulmonary resuscitation (CPR), we continuously measured cerebral blood flow (CBF), brain tissue oxygenation (StO2), cerebral metabolic rate of oxygen (CMRO2), and cerebral electrical activity (electrocorticography; ECoG). Multiple phases of cerebral hemodynamic recovery, with different degrees of mismatch between CBF and CMRO2, were observed following CPR. At 1 min post-resuscitation, we observed that the ratio CBF/CMRO2 is indicative of CA duration/severity and prognostic (with 87% accuracy) of short-term neurological recovery measured by the re-initiation of ECoG activity. These measurements provide the earliest known metrics for assessment of CA severity and prognosis post-CPR. Interestingly, the accuracy of this information is lost beyond 2-3 minutes post-CPR, highlighting a critical, easily overlooked, period immediately post-CPR. These metrics do not require pre-resuscitation data, underscoring translational potential in emergency-response settings when pre-CA information is unavailable. These metrics encourage validation in human studies, potentially offering real-time feedback during CA/CPR to optimize neurological outcome.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.