Monitoring of intracranial pressure in patients with traumatic brain injury

Abstract

Since Monro published his observations on the nature of the contents of the intracranial space in 1783, there has been investigation of the unique relationship between the contents of the skull and the intracranial pressure (ICP). This is particularly true following traumatic brain injury (TBI), where it is clear that elevated ICP due to the underlying pathological processes is associated with a poorer clinical outcome. Consequently, there is considerable interest in monitoring and manipulating ICP in patients with TBI. The two techniques most commonly used in clinical practice to monitor ICP are via an intraventricular or intraparenchymal catheter with a microtransducer system. Both of these techniques are invasive and are thus associated with complications such as hemorrhage and infection. For this reason, significant research effort has been directed toward development of a non-invasive method to measure ICP. The principle aims of ICP monitoring in TBI are to allow early detection of secondary hemorrhage and to guide therapies that limit intracranial hypertension (ICH) and optimize cerebral perfusion. However, information from the ICP value and the ICP waveform can also be used to assess the intracranial volume-pressure relationship, estimate cerebrovascular pressure reactivity, and attempt to forecast future episodes of ICH.

Keywords: ICP; TBI; autoregulation; compliance; non-invasive monitoring.

Figure 1

The inter-relationship between primary and secondary injury in TBI is shown. Secondary physiological insults can potentiate ischemia and lead to exacerbation of secondary injury. ICP = intracranial pressure, adapted from Mass et al. (2).

Figure 2

Cerebral volume–pressure curve showing the exponential relationship between ICP and an increase in volume of one of the intracranial components.

Figure 3

Log ICP vs. intracranial volume relationship defined by Marmarou (14). The pressure–volume index (PVI) is the notional volume (milliliters), which when added to the craniospinal volume causes a 10-fold rise in ICP.

Figure 4

Formulas for deriving the pressure–volume index (PVI), volume–pressure response (VPR), and the CSF outflow resistance (R_o), where P₀ is the baseline CSF pressure, P_p is the peak pressure resulting from a bolus volume injection V₀, and P₂ refers to the pressure point on the return trajectory at timet₂.

Figure 5

ICP waveform recorded from a Raumedic intraparenchymal catheter and displayed beneath an arterial waveform recorded from the radial artery in a patient with TBI. CRAN = intracranial pressure, ABP = arterial blood pressure, P₁ = percussion wave, P₂ = tidal wave, P₃ = dicrotic wave.

Figure 6

Volume–pressure relationship and equation are shown. Adapted from Avezaat and Van Eijndhoven (31). Craniospinal volume–pressure relationship demonstrating that for the same increase in craniospinal volume (dV _e) the ICP pulse amplitude (dP) increases when total craniospinal volume (V _e) increases. This is due to the exponential nature of the curve, which is described mathematically by the equation below the figure, where E₁ is the elastance coefficient and determines the elastance at a given pressure. P_eq = intracranial equilibrium pressure, P₀ = ICP at zero elastance.

Figure 7

ICP_plse versus ICP relationship [adapted from Avezaat and van Eijndhoven (31)]. ICP_plse plotted against ICP, demonstrating a direct linear relationship. A breakpoint occurs at an ICP of approximately 60 mmHg where the slope of the relationship increases.

Figure 8

Cerebral autoregulation. Illustration of the maintenance of cerebral blood flow across a range of cerebral perfusion pressures.

Figure 9

Electrical equivalence circuit of the Ursino model (42). CBF (q) enters the intracranial space at systemic arterial pressure (P_a). It is subject to arterial resistance (R_a) and the cerebrovascular bed has some storage capacity (C_a). CBF is then through proximal (R_pv) and distal (R_dv) venous resistance. Venous pressure (P_v) is assumed to equal ICP (P_ICP). P_ICP is dependent upon the volume stored in intracranial compliance (C_IC). This is dependent upon blood volume in C_a, CSF inflow (q_f) through inflow resistance (R_f), and CSF outflow (q_o) through outflow resistance (R_o), which is itself dependent upon venous sinus pressure (P_vs). The system can be disturbed by mock CSF injection (I_i).

Figure 10

Electrical equivalent circuit of the Czosnyka model (43). This figure Illustrates the presence of three storage compartments (C_a = compliance of the great cerebral arteries, C_v = compliance of capillaries, and small veins, C_i = compliance of the CSF containers). Other parameters are arterial blood pressure (ABP), cerebral arterial pressure in the small arteries (P_a), pressure in the cortical veins (P_v), ICP (P_i), sagital sinus pressure (P_ss), resistance of great cerebral arteries (R_a), cerebrovascular resistance (CVR), resistance of cortical and bridging veins (R_b), CSF outflow resistance (R_CSF), and CSF secretion (I_t). The lower figure shows the autoregulatory relationship between CVR and CPP as predicted by the model.

Figure 11

Examples of the relationships between HMF and CPP during challenge with norepinephrine before and after fluid percussion injury (FPI). (A) Before FPI. Challenge with norepinephrine resulted in a response consistent with active vasoconstriction in that a negative correlation value (R = -0.77) and negative slope (m) of the regression line (m = -0.317 Hz/mmHg) between HMF and CPP were demonstrated. (B) After FPI. Consistent with passive vasodilation, challenge with norepinephrine resulted in positive correlation values (R = 0.34) and slope of regression line (m = 0.325).

Figure 12

Image from Lundberg’s 1960 publication on Continuous recording and control of ventricular fluid pressure in neurosurgical practice (8).

Figure 13

BANN generated probability distribution plots for the mean likelihood of a favorable clinical outcome for patient populations managed in two different centers. In this data, the optimal point at which to switch from one treatment strategy to the other in a given patient is at an MABP/ICP trend with a slope of approximately 0.13. Taken from Howells et al. (91).

Figure 14

Example of an optimal CPP range (CPPopt) derived from the most recent 4-h CPP and autoregulation index values.