Validation of a mathematical model for understanding intracranial pressure curve morphology.

The physiology underlying the intracranial pressure (ICP) curve morphology is not fully understood. Recent research has suggested that the morphology could be dependent on arterial cerebral inflow and the physiological and pathophysiological properties of the intracranial cavity. If understood, the ICP curve could provide information about the patient's cerebrovascular state important in individualizing treatment in neuro intensive care patients. A mathematical model based on known physiological properties of the intracranial compartment was created. Clinical measurements from ten neuro intensive care patients in whom intracranial arterial blood inflow, venous blood outflow and cerebrospinal fluid flow over the foramen magnum had been measured with phase contrast MRI, concomitant with ICP measurements were used to validate the model. In nine patients the mathematical model was able to create an ICP curve mimicking the measured by using arterial intracranial inflow and adjusting physiological parameters of the model. The venous outflow and cerebrospinal fluid (CSF) flow over the foramen magnum predicted by the model were within physiologically reasonable limits and in most cases followed the MRI measured values in close adjunct. The presented model could produce an ICP curve in close resemblance of the in vivo measured curves. This strengthens the hypothesis that the ICP curve is shaped by the arterial intracranial inflow and the physiological properties of the intracranial cavity.

Increased Intracranial Pressure Attenuates the Pulsating Component of Cerebral Venous Outflow.

BACKGROUND: The underlying physiology of the intracranial pressure (ICP) curve morphology is still poorly understood. If this physiology is explained it could be possible to extract clinically relevant information from the ICP curve. The venous outflow from the cranial cavity is pulsatile, and in theory the pulsatile component of venous outflow from the cranial cavity should be attenuated with increasing ICP. In this study, we explored the relationship between ICP and the pulsatility of the venous outflow from the intracranial cavity. METHODS: Thirty-seven neuro-intensive care patients that had been examined with phase-contrast magnetic resonance imaging regarding cerebral blood flow (CBF) through the internal carotid and vertebral arteries and venous flow in the internal jugular veins were retrospectively included. The pulsatility of the jugular flow was determined by calculating the venous pulsatile index. The results were correlated to clinical data registered in the patient data monitoring system, including ICP and cerebral perfusion pressure (CPP). RESULTS: CBF was 996 ± 298 ml/min, and the flow in the internal jugular veins equaled 67 ± 17% of the CBF, with a range of 22-97%. The venous pulsatile index correlated negatively to ICP (R = - 0.47 p = 0.003). The lowest flow in the internal jugular veins over the cardiac cycle (Fmin) was not correlated to ICP. Temperature, end-tidal CO2, MAP, and CPP were not correlated to venous pulsatility. CONCLUSION: An increase in ICP correlates to a lower pulsatility of the venous outflow from the cranial cavity. A lower pulsatility could be due to increased pressure requirements to compress intracranial veins with increasing ICP.

Best zero level for external ICP transducer.

BACKGROUND: Continuous monitoring of intracranial pressure (ICP) was introduced in the 1950s. For correct ICP recordings, the zero-reference point for the external pressure gauge must be placed next to a head anatomical structure. We evaluated different anatomical points as zero reference for the ICP device at different head positions and their relation to brain centre (BC), foramen of Monro (Monro), and brain surface. METHODS: Patients referred for neuroimaging due to e.g. headache all having normal 3D MRI scans were selected. Monro, BC, Orbit(O), external auditory meatus (EAM), and orbito-meatal (OM) line were identified and projected to mid-sagittal, or axial images. Each scan was evaluated like lying supine, 45° head elevations, upright, and 45° lateral position. Distances from skin to brain surface, BC, and Monro were measured. All values are presented as mean ± SD and/or range in millimetre. For conversion to mmHg, millimetre was multiplied by 0.074. RESULTS: Twenty MRI scans were examined. A zero reference at EAM or glabella was ideal at BC when head was strict supine or in the lateral position. At 45° head elevation, an overestimation of the BC-ICP by 4.8 ± 0.8 and in upright 5.6 ± 0.5 mmHg was found, and 45° lateral underestimated ICP-BC by 6.3 ± 1.0 mmHg. Monro was situated 45 ± 5 mm rostral to the mid-OM line and 24 (18-31) mm inferior and 13 (8-17) mm in front of BC. A zero-reference point aligned with the highest point of the head underestimated BC-ICP and Monro-ICP. If the ICP reading was added 5.9 or 6.3 mmHg, respectively, a deviation from BC-ICP was ≤ 1.8 mmHg and Monro-ICP was ≤ 0.9 mmHg in all head positions. CONCLUSIONS: EAM and glabella are defined anatomical structures representing BC when strict supine or lateral but with 12 mmHg variation with different head positions used in clinical practice. The OM line follows Monro at head elevation, but not when the head is turned. When the highest external point on the head is used, ICP values at brain surface as well as Monro and BC are underestimated. This underestimation is fairly constant and, when corrected for, provides the most exact ICP reading.

The intracranial pressure curve correlates to the pulsatile component of cerebral blood flow.

Current methods to measure cerebral blood flow (CBF) in the neuro critical care setting cannot monitor the CBF continuously. In contrast, continuous measurement of intracranial pressure (ICP) is readily accomplished, and there is a component of ICP that correlates with arterial inflow of blood into the cranial cavity. This property may have utility in using continuous ICP curve analysis to continuously estimate CBF. We examined the data from 13 patients, monitored with an intraventricular ICP device determining the pulsatile amplitude ICPamp as well as the area under the ICP curve (AUCICP). Using an elastance measurement, the ICP curve was converted to craniospinal volume (AUCΔV). The patients were examined with Phase Contrast Magnetic Resonance Imaging (MRI), measuring flow in the carotid and vertebral arteries. This made it possible to calculate CBF for one cardiac cycle (ccCBFMRtot) and divide it into the pulsatile (ccCBFMRpuls) and non-pulsatile (ccCBFMRconst) flow. ICP derived data and MRI measurements were compared. Linear regression was used to establish wellness of fit and ANOVA was used to calculate the P value. No correlation was found between ICPamp and the ccICPMRpuls (P = 0.067). In contrast there was a correlation between the AUCICP and ccCBFMRpuls (R2 = 0.440 P = 0.013). The AUCΔV correlated more appropriately with the ccCBFMRpuls. (R2 = 0.688 P < 0.001). Our findings suggests that the pulsatile part of the intracranial pressure curve, especially when transformed into a volume curve, correlates to the pulsatile part of the CBF.

ICP curve morphology and intracranial flow-volume changes: a simultaneous ICP and cine phase contrast MRI study in humans.

BACKGROUND: The intracranial pressure (ICP) curve with its different peaks has been extensively studied, but the exact physiological mechanisms behind its morphology are still not fully understood. Both intracranial volume change (ΔICV) and transmission of the arterial blood pressure have been proposed to shape the ICP curve. This study tested the hypothesis that the ICP curve correlates to intracranial volume changes. METHODS: Cine phase contrast magnetic resonance imaging (MRI) examinations were performed in neuro-intensive care patients with simultaneous ICP monitoring. The MRI was set to examine cerebral arterial inflow and venous cerebral outflow as well as flow of cerebrospinal fluid over the foramen magnum. The difference in total flow into and out from the cranial cavity (Flowtot) over time provides the ΔICV. The ICP curve was compared to the Flowtot and the ΔICV. Correlations were calculated through linear and logarithmic regression. Student's t test was used to test the null hypothesis between paired samples. RESULTS: Excluding the initial ICP wave, P1, the mean R 2 for the correlation between the ΔICV and the ICP was 0.75 for the exponential expression, which had a higher correlation than the linear (p = 0.005). The first ICP peaks correlated to the initial peaks of Flowtot with a mean R 2 = 0.88. CONCLUSION: The first part, or the P1, of the ICP curve seems to be created by the first rapid net inflow seen in Flowtot while the rest of the ICP curve seem to correlate to the ΔICV.