Cerebrovascular autoregulation

The Values of Cerebrovascular Pressure Reactivity and Brain Tissue Oxygen Pressure Reactivity in Experimental Anhepatic Liver Failure

Authors: Grözinger G, Schenk M, Morgalla MH, Thiel C, Thiel K, Schuhmann MU.

BACKGROUND: We investigated in a porcine model of anhepatic acute liver failure (ALF), the value of two parameters describing cerebrovascular autoregulatory capacity, pressure reactivity index (PRx) and brain tissue oxygen pressure reactivity (ORx), regarding their power to predict the development of intracranial hypertension.
METHODS: In six pigs, hepatectomy was performed. Only one animal was sham operated. All animals received neuromonitoring including arterial blood pressure, intracranial pressure (ICP), and brain tissue partial oxygen pressure (P(br)O(2)). The average time of neuromonitoring was 31.0 h. Cerebral perfusion pressures (CPP), cerebrovascular pressure reactivity index (PRx) and brain tissue oxygen reactivity index (ORx) were calculated.
RESULTS: Perioperative disturbance of AR improved within 4 h after surgery. From 6 to 16 h post hepatectomy, ICP did slowly increase by 4 mmHg from baseline; CPP remained stable around 40 mmHg. PRx and ORx, however, indicated in this period a progressive loss of AR, reflected in a decrease of P(br)O(2) despite unchanged CPP. Beyond 16 h, ICP rose quickly. At CPP levels below 35 mmHg, P(br)O(2) fell to ischemic levels.
CONCLUSIONS: The loss of cerebrovascular autoregulatory capacity, indicated by a rise of PRx and ORx precedes the final crisis of uncontrollable intracranial hypertension in this animal model by hours. During this phase cerebral blood flow, as reflected in tissue oxygenation, deteriorates despite unchanged CPP. Monitoring of AR during ALF therefore seems to carry the power to identify a risk for development of critical CBF and intracranial hypertension.

Modeling and estimation for non-invasive monitoring of intracranial pressure and cerebrovascular autoregulation

Author: Kashif, Faisal M. 

Brain tissue is highly vulnerable to unbalanced oxygen demand and supply. A few seconds of oxygen deficit may trigger neurological symptoms, and sustained oxygen deprivation over a few minutes may result in severe and often irreversible brain damage. The rapid dynamics coupled with the potential for severe injury necessitate continuous cerebrovascular monitoring in the populations at greatest risk for developing or exacerbating brain injury. Intracranial pressure (ICP), which is the pressure of the cerebrospinal fluid, is a vitally important variable to monitor in a wide spectrum of medical conditions involving the brain, such as traumatic brain injury, stroke, hydrocephalus, or brain tumors. However, clinical measurement of ICP is highly invasive, as it requires neurosurgical penetration of the skull and placement of a pressure sensor in the brain tissue or ventricular spaces. Measurement of ICP is thus currently limited to only those patient populations in which the benefits of obtaining the measurement outweigh the significant attendant risks, thus excluding a large pool of patients who could potentially benefit from ICP monitoring. The primary goal of our work is to address the non-invasive monitoring of ICP. A secondary aim of this work is to develop methods for the assessment of cerebrovascular autoregulation, which is the innate ability of the vasculature to maintain cerebral blood flow in the face of changes in cerebral perfusion pressure. Cerebrovascular autoregulation is often impaired in patients with brain trauma or stroke, and also in pre-term neonates, as their cerebrovascular system is not fully matured. We develop methods for non-invasive, continuous, calibration-free and patientspecific ICP monitoring. Specifically, we present a model-based approach to providing real-time estimates of ICP and cerebrovascular resistance and compliance, for each cardiac cycle, from non- or minimally-invasive time-synchronized measurements of arterial blood pressure and cerebral blood flow velocity in a major cerebral artery. In the first step, our approach exploits certain features of cerebrovascular physiology, along with model reduction ideas, to deduce a simple mathematical model of the cerebrovascular system. In the second step, we develop algorithms to compute robust estimates of model parameters by processing the measured waveforms through the constraints provided by the models dynamic equation. For validation, our non-invasive estimates of ICP were compared against invasive measurements from 45 comatose brain-injury patients, with a total of 35 hours of data (over 150,000 beats), providing more than 3,500 independent ICP estimates. Our estimates track measured ICP closely over a range of dynamic variations. Pooling all independent estimates resulted in a mean estimation error (bias) of less than 2 mmHg and a standard deviation of error of about 8 mmHg. We also suggest how variations in estimated cerebrovascular resistance and compliance in response to variations in cerebral perfusion pressure may be used to provide novel approaches for assessment of cerebrovascular autoregulation.

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