Why is increased icp so clinically important

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Share on Pinterest A person can reduce ther risk of stroke by exercising regularly. Scientists identify new cause of vascular injury in type 2 diabetes. Adolescent depression: Could school screening help? Related Coverage. In particular, during ICP plateau waves in which ICP increases suddenly above 50 mm Hg and lasts longer than 5 minutes before returning to normal , vasodilation led to a decrease in CVR whereas during hypocapnia, vascular constriction led to an increase in CVR. In both cases, however, PI was found to increase.

In response to this observation, they concluded that PI is ultimately the product of the relationship between CPP, arterial pressure pulse amplitude, CVR, arterial compliance, and heart rate, and that it is a better indicator of CPP as opposed to ICP.

Nevertheless, models also provide some theoretical basis for the ICP-PI relationship in certain conditions. However, alterations to cerebral autoregulatory strength, vessel compliance, mean arterial pressure, and the state of intracranial dynamics specific to various neuropathological conditions can in some cases radically affect the slope, offset, and even linearity of the relationship.

Thus, it should not be expected that the same relationship ought to hold for all patients across a wide range of conditions, casting doubt on the practical reliability of using PI as an indicator of ICP. Fundamentally, methods that seek to use PI to estimate ICP attempt to do so by using linear regression to model the relationship between the two variables:. A large number of publications have attempted to estimate the value of the regression coefficients and apply their findings to the problem of noninvasive ICP estimation [ , , , , , , , , , , , , , , ].

A summary of such publications is provided in Table 4. For example, Behrens et al. Additionally, Shen et al. Based on the current state of research, it appears that PI by itself is likely too limited to be of broad clinical use as a means of estimating ICP across a range of neurological conditions. However, extreme values of PI may be useful for supporting the decision to begin invasive ICP monitoring. A natural extension of PI-based methods is to investigate whether the linear model can be improved by including additional hemodynamic variables in the linear regression.

They attributed this to the fact that there are too many dynamic variables in the injured brain that can not be properly accounted for in such a constrained model. A number of formulas have been proposed which can be used to estimate CPP CPPe based on noninvasively measured signals, which are listed below [ , , , ].

A summary of research exploring these formulas is provided in Table 5. This formula was based on the expectation that the ratio of mean flow to the pulsatile amplitude of flow should be roughly proportionally related to CPP after trying to approximately account for changes in the pulsatile amplitude of the arterial pressure waveform. The relationship assumes that the effect of compliance and ICP pulsatility on CPP are negligible, and the authors adopt the approach that this formula and its underlying assumptions are a hypothesis to be tested empirically.

Similarly to the Aaslid et al. Edouard et al. This formula was originally suggested for use as a method of assessing CPP in pregnant women [ ]. It is based on the Aaslid et al. A final formula is based on the critical closing pressure CrCP , which represents a threshold of ABP below which blood pressure in the cerebral microvasculature is insufficient to prevent the collapse of the vessel and subsequent cessation of flow [ ]. The equation for estimated CPP is:.

This formula was derived from an electrical circuit model of the cerebrovascular bed, which treated cerebrovascular resistance and cerebrovascular compliance as parallel resistive and capacitive elements, respectively [ ]. The constant coefficients were derived by fitting the formula according to an analysis of a database of retrospective TBI cases [ ]. More study may be needed, but currently, these methods do not appear to achieve the level of accuracy necessary for achieving widespread clinical adoption.

All of the formulas presented here rely on simplifying assumptions about the magnitude of the effect of various hemodynamic components, and thus should not necessarily be expected to hold in all cases where extreme values for the inputs or outputs are expected, where cerebral abnormalities or pathological conditions are present, or where the impact of confounding variables such as heart rate are unknown. This reliance on underlying assumptions represents a central challenge for methods based on simple formulas such as the ones presented here, and their use should be restricted to the specific conditions under which they have been empirically validated.

In the context of this review, model-based methods are effectively any method that uses a model more complicated than the simple linear models and formulas described in the previous sections. This category of methods is also the broadest category and can generally be broken down into two types of methods: theory-based and data-based methods. Theory-based methods typically involve some mathematical model designed to simulate intracranial states based on some initial state, boundary conditions, model parameters, and observable measurements.

These hemo- and hydro-dynamic models of the intracranial fluid dynamics are based on physical principles and have the advantage of not being wholly dependent on collecting a large amount of training data; however, they can be highly complex and their usefulness is not obvious in the absence of significant amounts of empirical validation.

Data-based methods are more common and rely on a large amount of training data that faithfully represents the target patient population in order to properly fit the model parameters. In principle, the relevant relationships can be learned by the model; however, the drawback of these types of approaches is that the validity of the resulting model is heavily dependent on having a large amount of appropriate training data due to the complex nature of the underlying physiology and the variation between individual subjects.

This is a significant issue as ICP data is inherently limited due to the invasive nature of ICP measurement and the difficulty in obtaining consistent, high-quality data. In an effort to retain the best of both worlds, some methods have attempted to combine aspects of theory-based and data-based models. A summary of the research into model-based methods is provided in Table 6. Due to the somewhat complicated interplay between ICP, arterial pressure, and cerebral hemodynamics, a large number of methods have attempted to incorporate arterial pressure measurements, which may provide complementary information to assist in measuring ICP [ , , , , , , , , , , , , , , , , ].

These methods are not technically noninvasive as they require the placement of a radial artery catheter for monitoring ABP; however, this procedure is typically already performed as part of standard care in neurointesive care units, and the risks associated with monitoring ABP via radial artery catheter are considered significantly less risky than invasive ICP monitoring.

The potential utility of this method was explored early on in a data-based method by Schmidt et al. However, follow-up efforts with much larger study groups consisting of TBI patients concluded that, while this method could estimate ICP with moderate accuracy, the relatively wide prediction interval as high as 17 mm Hg meant that this method alone was still insufficient for broad clinical application [ , ]. Significant additional work has been done to explore ways of improving this black box method.

It was found that including patient specific calibration, performed in a number of different ways, could be used to improve the accuracy of ICP estimation [ , ]. This result that including patient specific data in a data-based model improves accuracy seems somewhat unsurprising, but also of limited utility, as one of the common goals of estimating ICP noninvasively is to do so without the need for patient specific calibration.

Another method attempted to dynamically incorporate estimates of the state of cerebral autoregulation SCA into the model [ ]. Using this procedure, the authors found that the bias of the ICP estimation model decreased significantly compared with not including SCA information, from 7.

To deal with the complexity of estimating ICP for a hetereogeneous patient population, Schmidt et al. However, they concluded that none of the models showed a statistically significant improvement over the linear black box model.

Another approach relaxed the assumption of a linear relationship between ABP, ICP, and CBFV and instead adopted nonlinear kernel regression approaches, which resulted in a statistically significant reduction in bias for the test dataset from 6. Hu et al. Further work explored different linear and nonlinear mapping functions to identify how the performance of their data mining approach could be improved and found that nonlinear mapping functions could improve noninvasive ICP estimation over linear functions [ ].

Another study used a classification technique known as Ensemble Sparse Classifiers to diagnose intracranial hypertension in head-injured patients using morphological features extracted from CBFV waveforms [ ]. Kim et al. Additional data-based learning techniques including the use of support vector machines, linear discriminant analysis, and random forests using features extracted from ABP and CBFV waveforms have also been shown to achieve low error and promising classification accuracies in isolated cases [ , , ].

All of the model-based TCD methods discussed thus far have been considered data-based models, which do not require a detailed understanding of the physiology as an input assumption. These models implicitly rely on the assumption that information or features extracted from the TCD waveform are causally related to ICP and that the potentially complicated, nonlinear relationships that may depend on a whole host of physiological variables can then be learned by the model.

While the amount of promising results suggests that this assumption is likely true to good approximation, due to the lack of underlying physiological basis, significant additional independent validation is required to demonstrate clinical utility and determine the conditions that need to be satisfied for various models to be valid. In contrast to data-based models, theory-based models do attempt to model the physiological relationships based on a priori knowledge. One such approach to ICP estimation attempts to model the physiological relationships between ABP, CBF, and ICP using an electrical circuit analog, where pressures are represented by voltages and flows by currents, which we refer to as the Kashif model [ ].

The resistance and compliance of the cerebral vasculature are represented by single resistance R and capacitance C elements, respectively. The model simultaneously estimates ICP along with R and C by requiring the model constraints to be satisfied as closely as possible according to the obtained measurements over an estimation window consisting of at least five consecutive beats and under the assumption that ICP, R, and C are constant over that window.

Care is required to properly scale and time align the signals in order to accurately approximate the actual phase relationship between cerebral ABP and CBF. This theory-based modeling approach was validated on a sample of 37 patients with traumatic brain injury and concurrently measured invasive ICP, achieving a bias of 1.

Averaging bilateral ICP estimates reduced the bias to 1. A number of models have attempted to build upon the Kashif model. One refinement attempted to correct for hydrostatic fluid pressure differences associated with the different locations between the ABP and ICP pressure transducers by adjusting the ABP to account for the vertical height between the two pressure measurement locations [ ].

A different, simplified circuit model was developed by Lee et al. In this way, the capacitance element can be ignored as compliances have infinite impedance when the input is DC only, resulting in a model that consists of two simple resistance SR circuits, each made up of a single voltage source and single resistor, for estimating ICP.

This SR method does not require a long window and thus is more appropriate for detecting abrupt changes in ICP. However, though this method appeared to successfully track sudden ICP changes, it was only tested on simulated data and on patients performing Valsalva maneuver. Additionally, it did not include an adaptive algorithm for estimating model parameters such as the resistance of intracranial arteries, which account for the effects of autoregulation. To solve these issues, a follow-up study was conducted which employed an unscented Kalman filter to perform adaptive internal state estimation and validated the method on 11 TBI patients, obtaining a bias of 0.

These theory-based models may appear more attractive in that they are physiologically motivated rather than just the result of fitting some algorithmic model; however, the consequence of this approach is that they must contend with deciphering the complex dynamics of the cerebral vasculature. In order to do this, theory-based models must make simplifying assumptions that ignore some of the effects of variables such as vessel compliance, heart rate, and autoregulatory strength, which may become meaningful under exactly the kinds of extreme conditions these methods are expected to diagnose.

Attempting to combine aspects of both theory-based and data-based methods, Wang et al. They then integrated patient specific TCD-based CBFV data into the model using a Bayesian data assimilation framework that employed a regularizing iterative ensemble Kalman filtering method to tune model parameters and calibrate ICP prediction [ ]. This method was again validated initially against synthetic data before feasibility was demonstrated on two patients undergoing invasive ICP monitoring via EVD.

In both patients, prediction accuracy increased after assimilating CBFV data into the model and the researchers obtained a prediction error for mean ICP of less than 2 mm Hg in each patient, within the clinically accepted standard [ 31 , 32 , ].

However, significantly more work involving larger, heteregenous patient populations needs to be conducted in order to establish the efficacy of this method for ICP estimation. Though numerous studies have suggested promising results for the use of TCD in noninvasive ICP monitoring, additional study is needed to show that it has the requisite accuracy for clinical use, and there remain notable obstacles to its more widespread adoption. TCD has historically required a skilled technician and has shown both intra and inter-operator variability [ ].

ICP monitoring is a critically important component of proper neurocritical care and management of patients with acute brain injuries in order to prevent secondary insult and the potentially severe complications that can result.

Unfortunately, the procedures for monitoring ICP are invasive and carry their own sets of risks, and as a result, not all patients that could benefit from ICP monitoring receive it. As such, significant efforts have been made to develop a method for monitoring ICP noninvasively. Such a method would not necessarily replace invasive monitoring but could be used for pre-hospital triage, monitoring at-risk patients to assess the need for invasive monitoring, and in cases where invasive monitoring is deemed too risky or is otherwise contraindicted by other factors.

To date, no method appears to have achieved the level of accuracy, reliability, and independent validation necessary for widespread clinical acceptance.

However, a number of methods appear to hold promise and remain the subject of active, ongoing research. Of particular focus in this review were TCD-based methods, which are especially attractive due to the low cost, portability, and high temporal resolution of TCD. Raised intracranial pressure in acute viral encephalitis. Clin Neurol Neurosurg. PubMed Google Scholar. Severe traumatic brain injury in children-a single center experience regarding therapy and long-term outcome.

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A brain injury or another medical condition can cause growing pressure inside your skull. This dangerous condition is called increased intracranial pressure ICP and can lead to a headache. The pressure also further injure your brain or spinal cord. This kind of headache is an emergency and requires immediate medical attention. The sooner you get help, the more likely you are to recover. The symptoms of ICP may look like other conditions or medical problems.

Always see your healthcare provider for a diagnosis. Magnetic resonance imaging MRI used after the initial assessment uses magnetic fields to detect subtle changes in brain tissue content and can show more detail than X-rays or CT.

You may also be treated for the underlying cause of your intracranial pressure, which could be an infection, high blood pressure, tumor, or stroke. You can reduce your risk of certain underlying conditions that may lead to ICP such as high blood pressure, stroke or infection. It can lead to a headache. It can also further injure your brain or spinal cord. This kind of headache is an emergency. It requires medical care right away. The sooner you get help, the more likely you are to recover.

Hydrocephalus, which is an abnormal buildup of cerebrospinal fluid. This is the fluid around your brain and spinal cord. These symptoms may look like other health problems. Always see your healthcare provider for a diagnosis. To diagnose increased ICP, your healthcare provider will ask about your past health and do a physical exam.