Hello everyone, I'm Xiaofeng Jia, Professor of Department of Neurosurgery at the University of Maryland School of Medicine. So I would like to introduce functional and quantitative brain biomarker to improve outcomes after cardiorest. So cardiorest annually affects about 300,000 in the United States. So overall survival rate is pretty low, around 10 to 25%. Majority survivor remains comatose. If the patient didn't get proper treatment in minutes, permanent brain injury will happen. Usually patients will survive with persistent coma or vegetative status. Among survivors of the cardiorest, post ischemic brain injury is a leading cause of the death. The neurologist monitoring of the brain injury after cardiorest actually is quite complicated because this comatose survivor were complicated by the sedative and the paralytic agents. For these patients, bedside neurological exam in coma usually is limited. The neurophysiology and the clinical testing after cardiorest is delayed. This patient usually is taking care in general or cardiac ICU. Well, of course, sometime the need for the experts to interpret the test results such as EEG or SSEP or other complicated signals from the brain. During the most critical period of injury in comatose patient, there is no neuromotoring methodology for these survivors. We all know majority of these patients resuscitated from the cardiorest are treated with therapeutic hypothermia or targeted temperature management. Well, this brings additional challenge for the brain monitoring because this patient uses sedatives, paralytics to facilitate hypothermia. So patient with therapeutic hypothermia previously will verify the predicts for pro-neurological outcome after cardiorest now became not prognostically reliable. For example, loss of motor response on day three, while the response to the hypothermia is not clear. The monitoring challenge also weighs what's the exact parameters for the optimal hypothermia. All of these are not optimized yet. Julian Nolan's group recently in his review, Brain Injury After Cardiorest, highlighted that electrophysiology study including SSEP or EEG are good predicted tools to be used to predict outcome and detect unfavorable outcomes. Well, we established a rodent asphyxial cardiorest model and try to prior work to develop a real-time and easy marker for the brain injury to quantify the EEG. So here we show the here is left side and right side is from representative EEG from two rats underwent seven minutes cardiorest, which in rats lead to moderate brain recovery. So we can see from here the EEG baseline, both pretty similar, and the EEG silence after the cardiorest is the same. Then gradually we can see both groups start bursting and bursting with a longer duration in both groups. Gradually come with continuous EEG in both groups and partially recovered EEG in both groups as well. We can feel this EEG may be more actively recovered than this one, but we cannot conclude for sure. So with the expertise from the bioengineering department from the Johns Hopkins University, we developed the QEEG-IQ, which using entropy. So modified entropy indicated here is a baseline which has higher entropy, EEG silence when during the recovery, it started from low entropy gradually to increase the entropy. So with this one, we're using QEEG-IQ. Now we can see these two representative complicated EEG, we can transform to a more real-time and easy, simple IQ, EEG-IQ. We can show you here hypothermia, yes, real-time we do see that has better recovery than the normal semi-animal. So with that, we can potentially use this marker to improve the outcome. For example, we tested the two different therapeutic hypothermia. The blue one here indicate a traditional hypothermia, which we started one hour after resuscitation like clinically we normally see in this patient. While this is the red one, we can see here is immediately started hypothermia after resuscitation and lasted for six hours, while traditional hypothermia will last for 12 hours. The EEG-IQ, we see this has a better electrophysiology recovery, while we also have NDS, which is Neurological Deficit Scores. This group has higher NDS, that means better neurological recovery. And also, of course, with the better neuropathological recovery, identical by the decreased ischemic neurons. So while we try to evaluate this IQ, EEG-IQ as early electrophysiology marker, so we had more animal different group, we including normalthermia, we including hypothermia 232-234 immediate after resuscitation, and we have hypothermia, which had 39 degree right after resuscitation, all lasted for six hours. And we can see from here, some rats which died before 72 hours, and the survivor means lasted for whole 72-hour experiment period. The dead rats overall had lower QEG-IQ compared to the survivors. And if we separate every half hour, we can see these survivors have persistent higher QEG-IQ compared to the dead rats. If we pull all the rats treated with hypothermia, hypothermia, normalthermia, and we separated by good outcome and the bad outcome at 72 hours. We can see this is actually surprising to us is the rats, they started 30 minutes after resuscitation. Usually, these rats will have a comatose for four to six hours. At 30 minutes after cardio rest, we can see these rats have already different significantly in the QEG-IQ between good animal and the bad outcome animal. And this significance lasts from 30 minutes until 72 hours. If we use one-hour post-resuscitation, I will see a cut-off point IQ higher than 0.523, which yielded 81.8 sensitivity and 100 specificity for good outcomes. AUC is 0.886. So this actually give us said QEG-IQ could be used as early electrophysiology marker, a simplified but effective to track the outcome. In the clinically, we know the EEG actually divided by five common bands, different EEG bands from delta to gamma. So we will separate and develop a second called SIQ sub-band IQ EEG. So we separated the five band and do association and realize that actually the gamma band SIQ is the most significant part and which also associate most significantly with functional outcome. That is also the more associated with the higher level neural activity. So we know EEG works pretty well and has been used applied, I mean, in the clinical widely. But why we still need more marker, electrophysiology marker? If there are any good electrophysiology markers, yes, SSEP somatosensory evoked potential. So the SSEPs are more resistant to sedative drugs compared to the EEG. And it's also non-invasive and easy to acquire. Well, currently we know absence of both bilateral N20 more than 24 hours in the data, a bad outcome after cardiorest, while when N20 present, you know, they have a half-half chance to have a good recovery. So the previously now we can noticing here is N20 in the human equal to N10 in the rats, which we will using rodent model to present the next results here. So we know initially we started using N10 amplitude, which we can do the quantification, we can indicate it as a good, as a vast bad outcomes is a dynamic in the involved in a predictive version. And the early SSEP, which could be indicator from the injury, however, it takes challenge and difficult calculating of the peak and the measures by the amplitude. So with more advanced signal processing technique, we developed another innovative measurement called phase space area, which PSA. So we transfer the waveform to the shape, the big area means bad outcome, and the smaller is worse outcome indicative there is a temporal change of the waveform. So we can see here is PSA actually very precisely predicted the differentiate good and the bad outcome after cardiorest and was able to objectively check the recovery of the evoked response and the differentiate between good and the poor neurological outcome of cardiorest. And with that, we also could use the PSA to detect the treatment with hypothermia and the therapeutic beneficial, which actually was demonstrated by QEE-SSEP-PSA, which also this is two hours SSEP-PSA strongly correlated with 72 hours and DS. So we, with quantitative EEG and the quantitative SSEP, now we move for the cerebral blood flow. Quantitative cerebral blood flow take advantage of a new technology, novel laser speckle image. So with that one, we could using this, we could transform this actually is quite not clear to high spatial temporal resolution. This is real time. So with this technique, we can differentiate here is arterial vein and the capillaries in different version and in a dynamic version, we can see the changes. We also could a dynamic relative CBF changes after cardiorest. We could see that in a wild field of view with our current novel laser speckle image. So we can see actually hypoperfusion, they have changes and the hypothermia, they also have different change, which may predict the future intervention based on the CBF. So we concluded that quantitative brain monitoring technique, including QEEG and QSSEP can be utilized to aid emergency and critical care physicians in monitoring diversity aspects related to brain resuscitation. And this technique could lead to improved survival and the neurological outcomes after cardiorest. Of course, like we previously reviewed and summarized. It's not one single monitoring technique will work for all. A multi-model algorithm composed of neurological examination, EEG based quantitative testing SSEP in conjunction with newer mechanical resources imaging sequence, if available, holds promise for accurate prognostication in CA patients treated with therapeutic hypothermia. With that, I would like to acknowledge the funding support from NIH, RO1s and the hard work from the people from the JAR lab. And thank you for your attention.

functional brain biomarkers

quantitative brain biomarkers

cardiopulmonary resuscitation

brain injury monitoring

neurological outcomes