Search

Hyperventilation (HV) therapy uses vasoconstriction to reduce intracranial pressure (ICP) by reducing cerebral blood volume. However, as HV also lowers cerebral blood flow (CBF), it may provoke misery perfusion (MP), in which the decrease in CBF is coupled with increased oxygen extraction fraction (OEF). MP may rapidly lead to the exhaustion of brain energy metabolites, making the brain vulnerable to ischemia. MP is difficult to detect at the bedside, which is where transcranial hybrid, near-infrared spectroscopies are promising because they non-invasively measure OEF and CBF. We have tested this technology during HV (∼30 min) with bilateral, frontal lobe monitoring to assess MP in 27 sessions in 18 patients with traumatic brain injury. In this study, HV did not lead to MP at a group level ( p > 0.05). However, a statistical approach yielded 89 events with a high probability of MP in 19 sessions. We have characterized each statistically significant event in detail and its possible relationship to clinical and radiological status (decompressive craniectomy and presence of a cerebral lesion), without detecting any statistically significant difference ( p > 0.05). However, MP detection stresses the need for personalized, real-time assessment in future clinical trials with HV, in order to provide an optimal evaluation of the risk-benefit balance of HV. Our study provides pilot data demonstrating that bedside transcranial hybrid near-infrared spectroscopies could be utilized to assess potential MP.

Intracranial pressure (ICP) is an important parameter to monitor in several neuropathologies. However, because current clinically accepted methods are invasive, its monitoring is limited to patients in critical conditions. On the other hand, there are other less critical conditions for which ICP monitoring could still be useful; therefore, there is a need to develop non-invasive methods. We propose a new method to estimate ICP based on the analysis of the non-invasive measurement of pulsatile, microvascular cerebral blood flow with diffuse correlation spectroscopy. This is achieved by training a recurrent neural network using only the cerebral blood flow as the input. The method is validated using a 50% split sample method using the data from a proof-of-concept study. The study involved a population of infants ( n  = 6) with external hydrocephalus (initially diagnosed as benign enlargement of subarachnoid spaces) as well as a population of adults ( n  = 6) with traumatic brain injury. The algorithm was applied to each cohort individually to obtain a model and an ICP estimate. In both diverse cohorts, the non-invasive estimation of ICP was achieved with an accuracy of <4 mm Hg and a negligible small bias. Further, we have achieved a good correlation (Pearson's correlation coefficient >0.9) and good concordance (Lin's concordance correlation coefficient >0.9) in comparison with standard clinical, invasive ICP monitoring. This preliminary work paves the way for further investigations of this tool for the non-invasive, bedside assessment of ICP.