Systems and methods for intracranial compliance measurement

Abstract

Systems and methods related to determination of patient parameters such as intracranial compliance (ICC) are generally described. In some instances, intracranial compliance (ICC) measurement methods and systems for patients undergoing invasive intracranial pressure (ICP) monitoring are provided. The system may take arterial blood pressure (ABP), cerebral blood flow (CBF), and ICP data as input. The input measurements may be fed to a model.

Description

[0001]SYSTEMS AND METHODS FOR INTRACRANIAL COMPLIANCE MEASUREMENT RELATED APPLICATIONS This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No.63 / 686,073, filed August 22, 2024, and entitled “Systems and Methods for Intracranial Compliance Measurement,” which is incorporated herein by reference in its entirety for all purposes. TECHNICAL FIELD Systems and methods related to determination of patient parameters such as intracranial compliance are generally described. BACKGROUND Various patient parameters such as intracranial compliance may be useful for guiding neurocritical critical care. Therefore, improved systems and methods related to determination of patient parameters such as intracranial compliance are desirable. SUMMARY Systems and methods related to determination of patient parameters such as intracranial compliance (ICC) are generally described. The subject matter of the present invention involves, in some cases, interrelated products, alternative solutions to a particular problem, and / or a plurality of different uses of one or more systems and / or articles. In one aspect, systems are provided. In some embodiments, the system comprises: at least one hardware processor; and at least one non-transitory computer- readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform: obtaining a set of data identifying (a) a measure of arterial blood pressure of a patient, (b) a measure of cerebral blood flow of the patient, and (c) a measure of intracranial pressure (ICP) of the patient; and outputting an indication, based at least in part on the set of data, of a measure of a value of intracranial compliance (ICC) of the patient. #14318346v1 In another aspect, non-transitory computer-readable storage media are provided. In some embodiments, at least one non-transitory computer-readable storage medium stores processor-executable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform: obtaining a set of data identifying (a) a measure of arterial blood pressure of a patient, (b) a measure of cerebral blood flow of the patient, and (c) a measure of intracranial pressure (ICP) of the patient; and outputting an indication, based at least in part on the set of data, of a measure of a value of intracranial compliance (ICC) of the patient. In another aspect, methods are provided. In some embodiments, the method comprises obtaining a set of data identifying (a) a measure of arterial blood pressure of a patient, (b) a measure of cerebral blood flow of the patient, and (c) a measure of intracranial pressure (ICP) of the patient; and outputting an indication, based at least in part on the set of data, of a measure of a value of intracranial compliance (ICC) of the patient. Other advantages and novel features of the present invention will become apparent from the following detailed description of various non-limiting embodiments of the invention when considered in conjunction with the accompanying figures. In cases where the present specification and a document incorporated by reference include conflicting and / or inconsistent disclosure, the present specification shall control. BRIEF DESCRIPTION OF THE DRAWINGS Non-limiting embodiments of the present invention will be described by way of example with reference to the accompanying figures, which are schematic and are not intended to be drawn to scale. In the figures, each identical or nearly identical component illustrated is typically represented by a single numeral. For purposes of clarity, not every component is labeled in every figure, nor is every component of each embodiment of the invention shown where illustration is not necessary to allow those of ordinary skill in the art to understand the invention. In the figures: FIG.1 shows a plot of a nonlinear intracranial pressure-volume characteristic obtained by inflating a balloon inside the cranial space in a rabbit model. The nonlinear characteristic indicates one reason why it may be advantageous in some instances to jointly measure ICC with intracranial pressure (ICP). #14318346v1 FIG.2 shows an electrical analogue of an example of a cerebral hemodynamics model incorporating the ICC, according to some embodiments. ^a: arterial bloodpressure (ABP); ^ ̇: CBF; ^1: vascular and brain compliance; ^2: linearized ICC; ^:cerebrovascular resistance (CVR); ^v: venous pressure; ^i: ICP; ^dc: DC operating point for linearized ICC. The linear capacitors form an effective compliance, ^e. FIGS.3A-3C show illustrations of nonlimiting examples of measurement techniques that can be used for ICC measurement. FIG.3A: ICP measurements are acquired using invasive techniques. Invasive pressure measurement transducers may be placed in different anatomical locations. The parenchymal and ventricular measurement systems are considered clinical gold-standard measurements. FIG.3B: CBF is acquired in noninvasively in a cerebral vessel or one feeding the cranial space, such as the carotid artery. This may be done, for instance, using ultrasound insonation. FIG.3C: ABP data may be acquired using radial arterial catheters (RAC) as is common practice in neuro- intensive care units (ICUs). FIG.4A shows a plot of an invasive ICP measurement in a rabbit. Mean minute- by-minute ICP as solid black dots. Standard deviation in shading. FIG.4B shows a reference (circles) and model-based (solid curve) ICC estimates and unit standard deviation. ICC shows greater percentage change than ICP even while ICP remains below 20 mmHg clinical threshold. FIG.5A is a block diagram of an illustrative computer system that may be used in implementing some embodiments of the technology described herein. FIG.5B is a block diagram of an illustrative system comprising a computing device and a probe that may be used in implementing some embodiments of the technology described herein. FIG.5C shows a flowchart of an example of a method for outputting an indication of a measure of a value of intracranial compliance (ICC) of a patient, which can be part of computer-readable instructions in a storage medium, according to some embodiments; FIGS 6A-6D: FIG.6A is an anatomical depiction of the cerebrovascular system; FIG.6B is the corresponding three-compartment representation of FIG.6A; FIG.6C shows a plot of the nonlinear pressure-volume relationship of the craniospinal #14318346v1 compartment, linearized about the mean ICP, pī; FIG.6D shows the equivalent electrical analogue, according to some embodiments. FIGS.7A-7D: FIG.7A shows an illustration of the ABP waveform acquired for one subject. The inset shows a zoomed-in segment illustrating the waveform-nature of the data. FIG.7B shows corresponding CBF in mL / s; FIG.7C shows the ICP tracing (circles) acquired by inflating balloon B1 according to the volume profile shown (solid line); FIG.7D shows the corresponding ICC measurements (larger circles) calculated by observing the ICP change in response to transient inflation of balloon B2 and the associated CVR (smaller circles) calculated from the measured ABP, CBF, and ICP, according to some embodiments. FIGS.8A-8B show pressure-volume characteristics recorded for the inflation profile of FIG.7C with (FIG.8A) step and (FIG.8B) continuous inflation of balloon B1. Arrows indicate paths taken during infusion and volume withdrawal, respectively, according to some embodiments. FIGS.9A-9C show ICP-assisted intracranial compliance estimation results in rabbit model, according to some embodiments. Each figure represents estimates for individual rabbits. ICC estimates are shown in with the smaller circles while ground- truth measurements are shown with the larger circles. The corresponding ICP and CVR are also shown for reference. The band between ~85-105 minutes in FIG.9B indicates region where ABP waveform exhibited clogging artifact. FIG.10 shows a Bland-Altman plot for ICC estimates determined with known ICP and CVR, according to some embodiments. DETAILED DESCRIPTION Systems and methods related to determination of patient parameters such as intracranial compliance (ICC) are generally described. In some instances, intracranial compliance (ICC) measurement methods and systems for patients undergoing invasive intracranial pressure (ICP) monitoring are provided. The system may take arterial blood pressure (ABP), cerebral blood flow (CBF) (e.g., volumetric CBF in some instances rather than cerebral blood flow velocity), and ICP data (e.g., as waveforms) as input. The input measurements may be fed to a model (e.g., a physiologic model). The model #14318346v1 may encompass cerebral blood flow dynamics and cerebrospinal pressure-volume characteristics, which may obtain robust ICC estimates. Intracranial pressure (ICP) monitoring has long been a cornerstone of neurocritical care. Potentially fatal elevations in ICP can occur after conditions such as traumatic brain injury (TBI) or stroke, requiring continuous ICP monitoring for timely interventions. Although the craniospinal space can buffer moderate changes in the ICP, this capacity can exhaust fairly quickly. Therefore, it has been realized in the context of this disclosure that ICP measurements should not be interpreted in isolation. Instead, it has been realized that it may be beneficial for the buffering capacity to also be quantified (e.g., simultaneously) through intracranial compliance (ICC) measurements (e.g., as continuous measurements). As shown in FIG.1, starting from the unperturbed state, an increase in the volume of one cranial compartment (blood volume, brain tissue, cerebrospinal fluids), for instance due to an expanding hematoma, only leads to a moderate increase in ICP due to the inherent buffering mechanisms. Once this capacity is exhausted, even a small increase in cranial volume can lead to dangerously high ICP. Existing conventional ICC measurement techniques typically involve infusing a known volume of fluid into the cranial space and observing the resulting change in ICP. Such techniques carry a risk of infection, are time-consuming, require neurosurgical expertise, and lead to spot-measurements only. Additionally, injection of a volume of fluid into a patient’s ventricular space carries the risk of further elevating ICP when the buffering capacity might be exhausted. Surrogates for ICC determination have been developed for patients already undergoing invasive ICP monitoring. These methods revolve around analysis of and feature extraction from ICP waveforms, resulting in continuous, albeit indirect assessment of the buffering capacity of the craniospinal fluid system. Such approaches do not yield measures of the ICC in physiological units, making inter- and long-term intra-patient comparisons difficult. ICC measurements and their surrogates, therefore, have not transitioned into standard clinical care and simultaneous ICP-ICC monitoring thus remains an unfulfilled desire in clinical neuroscience. Aspects of this disclosure are directed to systems and methods that, in some embodiments, employ a model-based approach for continuous estimation of the ICC (e.g., continuous estimation) in absolute physiological units of volume divided by pressure (e.g., of mL / mmHg) for patients undergoing ICP monitoring (e.g., invasive ICP #14318346v1 monitoring). Specifically, data from measurements of the ABP, CBF, and ICP may be employed in conjunction with a model (e.g., a lumped-parameter model) of cerebral circulation to estimate the ICC. This approach was extensively validated in an animal model where ground-truth invasive ICP and ICC measurements were available. It is believed that this is the first demonstration of a model-based approach that can yield continuous ICC estimates in their respective physiological units. Of the required input data, the ABP and ICP measurements are already available at the bedside, while the CBF measurements can be acquired noninvasively using, for instance, ultrasound-based measurement. The systems of this disclosure thus do not increase the risk of cranial infection to patients whilst allowing continuous ICC monitoring. It thus presents a significant advancement in the state-of-the-art of neuromonitoring in patients undergoing in-hospital ICP monitoring for a wide range of pathologies. Moreover, the system of this disclosure improves neurocritical care technology by facilitating an ability to obtain convenient (and in some instances minimally-invasive) measurements of ICC as needed or desired (e.g., intermittently), which can alone or in combination with ICP measurements guide clinical treatment, standing in contrast with invasive approaches that do not permit a clinician to easily obtain such measurements nor to do so without increasing risk of complications. Aspects of the disclosure relate to special purpose computers programmed to perform the algorithms described herein. For example, a special purpose computer may be programmed with instructions for carrying out the algorithms described herein for determining ICC, among other algorithms provided herein. In some embodiments, a special purpose computer comprises a system (e.g., system 1000) including at least one hardware processor (e.g., processor 1910 discussed below) and at least one non- transitory computer-readable storage medium (e.g., computer-readable storage medium 1930 discussed below), among other components. Some aspects of this disclosure relate to systems comprising at least one hardware processor. Some aspects relate to non-transitory computer-readable storage media storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform steps relating to obtaining one or more sets of data and outputting an indication, based at least in part on the set of data, of a measure of a value ICC (e.g., in absolute units). FIGS.5A-5B #14318346v1 show illustrative block diagrams of some embodiments of such systems, which are described in more detail below. The system may use the set of data for identifying measures of two or more parameters related to distension of a vessel and blood flow through the vessel. Non- limiting parameters that can satisfy these criteria include, but are not limited to, a measure of arterial blood pressure and a measure of cerebral blood flow of a patient. As noted above, the measure of the cerebral blood flow may be cerebral blood flow velocity (CBFV). Alternatively or additionally, in some embodiments, the measure of the cerebral blood flow is or includes volumetric cerebral blood flow (CBF). CBF may provide certain advantages in some instances. For example, the system may identify a measure of CBF (e.g., as a waveform) as opposed to CBFV, which can allow for subsequent or simultaneous calculation of ICC (e.g., in absolute units), which, it has been realized in the context of this disclosure, can be advantageous compared to the use of relative measurements. Non-limiting techniques for making measurements and calculating parameters such as ICC relating to distension of a vessel and / or blood flow through the vessel are described in more detail below. Some embodiments involve use of volumetric cerebral blood flow (CBF) instead of cerebral blood flow velocity (CBFV). It has been realized that doing so can, at least in some instances, allow one to estimate the ICC in absolute units of volume divided by pressure (e.g., mL / mmHg). Absolute measurements may allow for long-term inter- and intra-subject comparisons that are not practical or possible with relative measurements. Furthermore, output of ICC in absolute units can improve care of patients suffering from or suspected of suffering from a neuropathological condition at least because clinical guidelines for monitoring and / or treatment tend to be conveyed in terms of absolute units, and so a system that output such information in absolute units may be more clinically actionable than those that output relative units. FIGS.3A-3C show examples of approaches for the system and methods of this disclosure. Patients’ ABP and ICP waveforms may be acquired using currently known techniques. For example, ultrasound imaging may be used for CBF measurements. As a specifical example, a clinical-grade ultrasound imaging device may be used to perform standard time-interleaved B-mode and Doppler measurements at a cerebral vessel or an extracranial vessel feeding the cranial space (for instance, the internal carotid artery). #14318346v1 The B-mode images may be processed to detect vessel edges and estimate vessel diameters. The velocity measurements from the Doppler data may be then integrated to estimate the volumetric flow. The resulting ABP, ICP, and CBF waveforms may then be passed to a model-based estimation scheme in predetermined time windows (e.g., 15- to 60-second recording windows). Thought not shown explicitly in FIGS.3A-3C, measurement of spinal ICP (e.g., during a spinal tap) is also a possible source of ICP data. In some embodiments, the measurements determined at the patient site used to create information that can be sent to the hardware processor include a B-mode measurement (e.g., at a cerebral vessel). In some embodiments, the measurements determined at the patient site used to create information that can be sent to the hardware processor include a color flow / Doppler measurement (e.g., at a cerebral vessel). These measurements may be used or determined using at least one sensor. For example, these measurements may be used with a probe such as an ultrasound probe. In some, but not all embodiments, at least some of the measurements are calibrated based on data from, for example, an arm-cuff. In other embodiments, other sensors may be used to determine such measurements. As mentioned elsewhere in this disclosure, the data obtained from the measurements received by the hardware processor (e.g., processor 1910 discussed below) may identify a measure of arterial blood pressure. For example, this data may be processed according to algorithms described herein to output a measure of arterial blood pressure. In some such embodiments, the data comprise arterial blood pressure waveforms. As also mentioned elsewhere in this disclosure, the data obtained from the measurements received by the hardware processor (e.g., processor 1910 discussed below) may be processed to identify a measure of cerebral blood flow (e.g., volumetric cerebral blood flow). In some such embodiments, the data are calculated via integration of a measure of cerebral blood flow velocity (CBFV). The integration may be performed, for example, using vessel diameter waveforms. Non-limiting examples of model-based techniques for such an integration are described in International Patent Application Publication No. WO2024 / 077201, published on April 11, 2024 and entitled #14318346v1 “System and Methods for Measurement of Parameters Such as Intracranial Pressure,” which is incorporated herein by reference in its entirety for all purposes. As noted elsewhere in this disclosure, some embodiments involve outputting an indication of a measure of a value of intracranial compliance (ICC) (e.g., based at least in part on the set of data obtained by the processor (e.g., processor 1910 discussed below) (e.g., from measurements from one or more probes))). Intracranial compliance (ICC) refers to the change in intracranial pressure in response to a change in intracranial volume. ICC can be expressed mathematically in terms of a change in volume divided by a change in pressure. ICC therefore captures the buffering capability of the system. A comparatively high ICC value suggests that buffering may still be adequate to compensate for intracranial volume shifts (e.g., maintaining ICP relatively constant). Conversely, a comparatively low ICC value can indicate that, for the same volume shift, ICP will increase more significantly. Accordingly, ICC can help characterize a subject’s operating point on the “pressure-volume” curve and can in some instances offer a potential early warning of rising ICP. Accordingly, in some instances, outputting (and in some instances displaying) a measure of ICC and displaying a measure of ICP (e.g., used as an input in the calculation of the ICC) can, in combination, provide greater insight into treatment choice for a subject than ICP alone. A reliable and convenient system to measure and / or monitor ICC (e.g., alone or in addition to ICP) could be helpful in determining whether the brain is in a compensated (stable or low-risk) state or in an uncompensated (unstable or high-risk) state in relation to further incremental volume increases. Determining ICC according to the embodiments described in the disclosure (e.g., based a set of data identifying (a) a measure of arterial blood pressure of a patient, (b) a measure of cerebral blood flow of the patient, and / or (c) a measure of intracranial pressure (ICP) of the patient) may be less invasive and therefore more convenient and accessible than conventional ICC measurement techniques such as volume-pressure tests in which fluid boluses are injected into the craniospinal space and ICP is concomitantly measured. The measure of the value of ICC output by the hardware processor (e.g., processor 1910 discussed below) in certain systems of this disclosure may be in absolute units and / or in relative units. Absolute units of ICC refer to units of volume divided by pressure, which are expressed in terms of volume divided by a quantity of force divided #14318346v1 by area. Accordingly, in some embodiments, the measure of a value in absolute units of ICC is in the units of mL / mmHg, mL / pascal, mL / bar, mL / PSI, mL / atmosphere, or a multiple thereof. In some embodiments, the measure of the value of ICC in absolute units is a measure of a value in absolute units of a regional ICC. However, the measure of the value of ICC in absolute units is a measure of a value in absolute units of a global ICC. In some embodiments, the system permits the determination of a measure of the ICC in absolute units. While measurements in relative units may allow one to track relative changes in ICC within the same patient over time, having these measurements in absolute physiological units may allow clinicians to compare a patient’s ICC against population norms and initiate treatment if the measured values start to deviate from such population norms. Obtaining absolute ICC values is facilitated by acquiring volumetric CBF waveforms, where the CBF may, for example, be taken to be the product of the vessel area and the blood flow velocity. In some embodiments, a model-based approach is employed to determine the measure of the value of ICC. The model may be mechanistic and be capable of estimating model parameters without requiring a set of training or population level data. In some embodiments, the value of ICC is calculated by feeding (a) an arterial blood pressure waveform, (b) a volumetric cerebral blood flow waveform, and / or (c) data identifying a measure of ICP (e.g., an ICP waveform) to a physiologic model (e.g., a modified physiologic model). For example, the measured ABP, ICP, and CBF data (e.g., some or all of which may be in the form of waveforms) may be fed to a physiologic model of cerebrovascular / cerebrospinal physiology (e.g., of cerebral blood flow), an example of which is shown in FIG.2. An appropriate parameter estimation technique can then be used to determine values for ^1, ^2, ^, and the ICP, ^i. The values for ^2 represent the ICC. Tilt-table studies were conducted in healthy volunteers and found that this method successfully tracked expected changes in ICC. Further, these estimates were compared with reference to invasively-acquired measurements in a rabbit model as illustrated in FIGS.4A-4B. The figures show the importance of joint ICP-ICC measurement: the ICC drops by a factor of 2 while the ICP remains below the clinical threshold of 20 mmHg; thus the ICC serves as a warning indicator of high ICP. The measures of ICC determined by this disclosure can, in some embodiments, be acquired #14318346v1 in a continuous fashion. The measures of ICC may have negligible estimation bias compared to reference measurements. While description of a physiologic model is described above, in other embodiments, a statistical model may be employed. For example, in some embodiments, a machine learning model is employed. As a specific example, in some embodiments, the value of ICC is calculated by feeding (a) an arterial blood pressure waveform, (b) a volumetric cerebral blood flow waveform, and / or (c) data identifying a measure of ICP (e.g., an ICP waveform) to a statistical model (e.g., a machine learning model). Furthermore, a special purpose computer system may be programmed with one or more of such statistical models. Examples of statistical models and programmed special purpose computer systems are described below in more detail in the section titled “Example Special Purpose Computer Systems Programmed with Statistical Models.” In some embodiments, the system comprises one or more probes. In some embodiments, the probes are configured to perform measurements at a patient site. The probes may be further configured to send information about the measurements to the at least one hardware processor (e.g., processor 1910 discussed below). In some embodiments, at least a portion of the set of data that identifies a measure of arterial blood pressure of the patient is based at least in part on the information about the measurements at a patient site. In some embodiments, at least a portion of the set of data that identifies a measure of cerebral blood flow of the patient is based at least in part on the information about the measurements at a patient site. In some, but not necessarily all embodiments, the system is configured to perform measurements of arterial blood pressure and cerebral blood flow via separate measurements at different patient sites – a first measurement at a first patient site to determine ABP (e.g., using a radial arterial catheter at the wrist) and a second measurement at a second patient site to determine cerebral blood flow velocity (e.g., using a transcranial Doppler (TCD) at the middle cerebral artery (MCA)). The patient site at which at least some of the measurements are performed may correspond to a single vessel or a portion thereof. The vessel may be an extracranial vessel or an intracranial vessel. Extracranial vessels may be particularly convenient for some embodiments. Examples of suitable vessels at which the measurements may be performed (e.g., in a localized manner) include, but are not limited to, a common carotid #14318346v1 artery, an internal carotid artery, a vertebral artery, a basilar artery, a middle cerebral artery, a posterior cerebral artery, and / or an anterior cerebral artery. As mentioned above, the measurements that can be used to calculate the parameters related to distension of a vessel or blood flow through the vessel (e.g., ABP and CBF or CBFV) may be performed by one or more probes. The probe(s) may comprise one or more sensors. The probe(s) may be configured to perform the measurements at one or more patient sites and send information about the measurements to the at least one hardware processor (e.g., processor 1910 discussed below). Any of a variety of types of probes may be used. The probe(s) may be a non-invasive probe. For example, at least one probe may not require the introduction of an instrument into the body of a subject. In some embodiments, the probe(s) comprises an ultrasound probe (e.g., configured to perform ultrasound imaging). In some embodiments, the ultrasound probe is configured to perform color flow imaging measurements, B-mode measurements, and / or pulsed-wave spectral ultrasound measurements. One non-limiting example of an ultrasound probe that can be used in some embodiments is a Butterfly iQ handheld ultrasound device, details of which are described in Sanchez, N., et al., (2021, February). “34.1 an 8960-element ultrasound-on-chip for point-of-care ultrasound” in 2021 IEEE International Solid-State Circuits Conference (ISSCC) (Vol.64, pp.480- 482), which is incorporated herein by reference. In some embodiments, ABP measurements (e.g., ABP waveforms) are measured using one of the one or more probes. In some embodiments, the data corresponding to the ABP is measured at a peripheral location. The peripheral location may be, for example one or more fingers and / or the wrist. The ABP probe may be configured to use noninvasive measurements (e.g., finger-cuff inflation and / or cuff-based techniques). Alternatively, the ABP probe may be configured to us minimally invasive measurements (e.g., indwelling catheters). As noted elsewhere in this disclosure, some embodiments involve inputting data indicating a measure of a value of intracranial pressure (ICP) of the patient (e.g., from measurements from one or more probes, such as an invasive probe in some embodiments). ICP refers to the hydrostatic pressure of cerebrospinal fluid (CSF), which is the fluid that surrounds and cushions the brain tissue of a human or animal and also resides and circulates through the brain’s ventricular system. When the ICP #14318346v1 becomes elevated in an individual, blood flow to the brain can become limited and lead to cerebral ischemic injury. Additionally, brain structures may become displaced (herniation) because of pressure differences within the cranial cavity or between the cranial cavity and spinal canal, which may potentially lead to coma, cessation of breathing, and / or death. Elevation of ICP may occur in various neuropathological conditions, including hydrocephalus, traumatic brain injury, hemorrhagic stroke, brain tumors, and / or metabolic conditions. In managing these types of neuropathological conditions, it can be important to monitor the ICP of the individual to assess the cerebrovascular and cerebrospinal state of the individual and to determine if the ICP becomes elevated to a point that puts the individual at a high-risk level. The obtained data corresponding to a measure of the value of ICP input to the hardware processor (e.g., processor 1910 discussed below) in certain systems of this disclosure may be in absolute units and / or in relative units. In the context of this disclosure, absolute units are understood to refer to the absolute physiologically relevant units corresponding to that parameter, as opposed to non-physiologically relevant units that merely correlate with the value of that parameter. For example, absolute units of ICP refer to units of pressure, which are expressed in terms of force divided by area. Accordingly, in some embodiments, the measure of a value in absolute units of ICP is in the units of mmHg, pascals, bars, pounds per square inch (PSI), atmospheres, or a multiple thereof. An example of a multiple of the aforementioned units is any unit that can be obtained by simply multiplying the units by a number. For example, a kilopascal is a multiple of pascal because it is obtained by multiplying a value in units of pascals by 0.001. As another example, a dyne / cm2is also a multiple of pascal because it is obtained by multiplying a value in units of pascals by 10. An example of a measure of ICP that is not in absolute units in the manner with which the term is used in the context of this disclosure would be an ultrasound time-of-flight measurement through a subject’s skull. The time-of-flight measurement is expected to be correlated with ICP to the extent that a faster time-of-flight is typically associated with a higher ICP. However, a measure of time-of-flight (in units of time), while correlated with ICP and therefore a relative measure of ICP, is not in physiologically relevant units of pressure and would not generally be useful in guiding monitoring and / or treatment of a patient. By contrast, #14318346v1 absolute units in terms of pressure (force divided by area) would be useful for guiding monitoring and / or treatment of a patient. As discussed elsewhere, some embodiments involve obtaining a set of data identifying a measure of intracranial pressure (ICP) of the subject (e.g., a patient). The use of a measure of a value of ICP (e.g., as a mean value and / or as a waveform) as an input (e.g., into the model such as physiological model) along with other parameters such as ABP and a measure of cerebral blood flow may permit relatively accurate non- invasive determination of the patient’s ICC (e.g., for monitoring and / or guiding clinical care). As a non-limiting example, in some embodiments, systems and methods for providing measures of ICC may use one or more computer processors to (a) apply a first weight to a measure of arterial blood pressure of a patient, (b) apply a second weight to a measure of cerebral blood flow of the patient, and (c) apply a third weight a measure of intracranial pressure (ICP) of the patient. Based on the first, second, and third weighted measures, the systems and methods for providing measures of ICC may calculate a measure of ICC. In some embodiments, the systems or methods store the measure of ICC in memory or may provide the measure of ICC as output (e.g., via one or more of a graphical user interface (GUI), an alert such as an audio and / or visual alert, or a prompt prompting a healthcare provider to provide a clinical intervention to the patient). Additional examples of providing measures of ICC using statistical models and programmed special purpose computer systems are described below in more detail in the section titled “Example Special Purpose Computer Systems Programmed with Statistical Models.” In some embodiments, at least a portion (or all) of the set of data that identifies the measure of ICP of the patient is obtained from an invasive measurement of ICP on the patient. The ICP may be measured invasively due, for example, to the patient being already identified as being at a high risk of or suffering from a serious neuropathological condition (e.g., based on examination by a healthcare provider and / or an output of a previous non-invasive measurement of ICP). The invasive measurement of ICP may be obtained from measurements acquired from one or more invasive probes. The one or more invasive probes may be configured to perform invasive measurements at a patient site and send information about the invasive measurements to the hardware processor (e.g., processor 1910 discussed below). Invasive ICP measurements have been shown to be highly accurate (providing quality data for the calculations described in this #14318346v1 disclosure). Moreover, the use of invasive techniques for measuring ICP may permit other procedures to be performed on the patient (e.g., simultaneously and / or in response to the value of an output indication), such as the removal of fluid to relieve pressure (e.g., via at least a portion of the same probe used for ICP measurement, such as a same catheter). Any of a variety of invasive measurements (e.g., made using one or more invasive probes) may be employed to determine a measure of a value of ICP. The invasive measurements may comprise breaking the skin and / or inserting at least a portion of a device (e.g., probe) into the body of the subject (e.g., patient). In some embodiments, the invasive measurement of ICP comprises placement of a probe in the intradural space of the patient (e.g., resulting in at least portion of the probe residing in or passing through the intradural space). In some embodiments, the invasive measurement of ICP comprises placement of a probe in the brain of the patient (e.g., resulting in at least a portion of the probe residing in or passing through at least a portion of the brain). In some embodiments, the invasive measurement of ICP comprises placement of a probe in the spinal dural sac of the patient (e.g., resulting in at least a portion of the probe residing in or passing through at least a portion of the spinal dural sac to facilitate measurement based at least in part on the pressure of the cerebrospinal fluid). One example of an invasive probe for obtaining data identifying a measure of the ICP of a patient is a catheter-based probe (e.g., for insertion into the patient). For example, the probe may be delivered via a catheter and configured to perform the external ventricular drain (EVD) technique, which involves placement of a fluid-filled catheter (e.g., a silastic catheter) into one or more ventricles for direct measurement of cerebrospinal fluid pressure. As another example, a pressure-sensitive probe coupled to a catheter may be placed (e.g., into the brain parenchyma to measure tissue pressure), with non-limiting examples including fiber-optic transducers and / or strain-gauge sensors (e.g., micro-strain gauge sensors). In some embodiments, the invasive measurement of ICP employed comprises drilling a hole through the skull to place a pressure sensitive probe and / or a fluid-filled catheter in the brain parenchyma and / or cerebral fluid spaces. In some embodiments, at least a portion (or all) of the set of data that identifies the measure of ICP of the patient is obtained from a non-invasive measurement of ICP on the patient. The non-invasively obtained set of data that identifies the measure of ICP #14318346v1 of the patient may then be used as an input in determining a measure indicative of the patient’s ICC. A non-invasive measurement may, in some instances, reduce risk of complications (e.g., infection) and / or be more convenient for the healthcare provider and / or patient. Any of a variety of non-invasive techniques for ICP measurement may be employed. One non-invasive technique uses physiologic model-based methods that relate a subjects’ ABP with cerebral blood flow velocity (CBFV) and ICP. The ABP is measured at a peripheral location such as the fingers or the wrist using noninvasive means (finger-cuff inflation) or using minimally invasive indwelling catheters. The CBFV is measured using transcranial doppler ultrasound in a cerebral artery. In other embodiments, ABP and volumetric cerebral blood flow (CBF) are measured at a single patient site, in some such instances using a single probe (e.g., an ultrasound probe). Another approach may involve placing of a non-invasive probe at an ear membrane such as at the tympanic ear membrane. Further examples of techniques for non-invasive ICP measurement are described in, for example, U.S. Patent Application Publication No. 2021-0121087, U.S. Patent Application Publication No.20220160327, and U.S. Patent No.11,166,643. In some embodiments, the indication of the measure of the value of ICC of the patient is output continuously. The continuous outputting of the measure of the value of the ICC of the patient may comprise outputting (e.g., and displaying) two or more (e.g., at least two, at least three, at least five, at least ten, at least fifty and / or up to one hundred or more) indications of the measure of a value of ICC within a time period of less than or equal to one hour, less than or equal to 30 minutes, less than or equal to 20 minutes, less than or equal to 10 minutes, less than or equal to 5 minutes, less than or equal to 2 minutes, less than or equal to 60 seconds, and / or as few as 30 seconds, as few as 15 seconds, or less. Combinations of these ranges are possible. The system may be configured to output the measure of the value of ICC of the patient continuously for a period of at least 30 seconds, at least one minute, at least two minutes, at least 5 minutes, at least 10 minutes, at least 20 minutes, at least 30 minutes, at least one hour, at least two hours, at least 4 hours, at least 10 hours and / or up to 24 hours, up to 48 hours, up to one week, or more. Combinations of these ranges are possible. In some embodiments, the system comprises a display. Examples of connectivity to the display are described in more detail below. The display may be configured to #14318346v1 communicate (e.g., to display) to a user (e.g., a healthcare provider) the indication of the measure of the value of ICC output from the at least one hardware processor (e.g., processor 1910 discussed below) (e.g., via displayed text, displayed graphics, and / or sound). In some embodiments, the display includes a graphical user interface (GUI). For example, the display may be configured to communicate (e.g., to display) to the user an indication indicating a measure of a value (e.g., in absolute units and / or in relative units) of intracranial compliance (ICC). In some embodiments, the display is configured to communicate (e.g., to display) to the user an indication indicating a measure of a value (e.g., in absolute units and / or in relative units) of intracranial compliance (ICC) and a measure of a value (e.g., in absolute units and / or in relative units) of intracranial pressure (ICP). As noted above, in some instances it can be advantageous and an improvement over conventional technologies for the indication of ICC output by the processor (e.g., processor 1910 discussed below) and communicated by the display in the contexts discussed in this disclosure (e.g., where ICP is already measured, e.g., invasively or non- invasively) to be in absolute units (e.g., units corresponding to volume divided by pressure). The display of the information to the user may facilitate the user in monitoring and / or treating the subject (e.g., for a neuropathological condition). In some embodiments, the display is configured to alert at least one user (e.g., a healthcare provider) based at least in part on the information output by processor (e.g., processor 1910 discussed below). For example, in some embodiments, the instructions stored in the computer-readable storage medium (computer-readable storage medium 1930 discussed below) may, when executed by the at least one hardware processor (e.g., processor 1910), further cause the hardware processor to generate an alert regarding the administration of a treatment for relieving a neuropathological or other condition based at least in part on the indication output from the processor indicating a measure of a value of ICC (e.g., alone or in combination the measure of ICP). In some such embodiments, the instructions may further be configured to, when executed, cause the processor to transmit the alert to the display. The transmission of the alert to the display may be performed such that the alert triggers the display to communicate (e.g., display) the alert to at least one user (e.g., via text, graphics, and / or sound). In some embodiments, the processor is caused to prompt, using the alert generated by the processor and transmitted to the display, at least one user to administer the treatment to #14318346v1 the subject. In some embodiments, the processor (e.g., processor 1910 discussed below) is caused to prompt, using the alert generated by the processor and transmitted to the display, the user(s) to begin, continue, and / or cease monitoring the subject. Such an alert may provide objective guidance to the user to care for a patient suffering and / or suspected of suffering and / or already being treated for suffering from a neuropathological condition such as a traumatic brain injury. The alert may be generated and transmitted to the display when the measures of a value of ICC (e.g., alone or in combination the measure of ICP) exceeds or falls below a threshold (e.g., a predetermined threshold or a user-entered threshold). For example, the prompt (which may be provided via a GUI as noted above), may be provided to (e.g., displayed and / or transmitted to) a healthcare provider and may prompt the healthcare provide to provide a clinical intervention to the patient. In some embodiments, in response to determining that the ICC measure exceeds or falls below a threshold ICC value, an ICC system or method may establish a remote communication link with an electronic device associated with a healthcare provider, and may transmit the ICC measure or an alert indicative of the ICC measure over the remote communication link. In some embodiments, at least one user (e.g., a healthcare provider) can obtain a measure of intracranial compliance (e.g., in absolute and / or relative units) of a subject using the system described in this disclosure. The subject may be a patient (e.g., suffering from or suspected of suffering from (or already being treated for suffering from) a neuropathological condition such as traumatic brain injury). In some embodiments, the subject is a patient who has already been determined by a clinician to be suffering from a potentially life-threatening cerebrovascular / cerebrospinal condition. Some aspects of this disclosure relate to the administration of a treatment to a subject (e.g., a patient) based at least in part on the measure a value of ICC (e.g., alone or in combination with the measure of ICP) obtained using the systems and methods described above. In some embodiments the subject is a human. In some embodiments, the subject is an animal (e.g., a non-human animal). For example, a user (e.g., a healthcare provider) may administer a treatment for relieving a neuropathological condition to a subject (e.g., a patient) based at least in part on the obtained measure. The neuropathological condition may be, for example, a traumatic brain injury (e.g., a severe traumatic brain injury) and / or a secondary injury following a traumatic brain injury (e.g., #14318346v1 intracranial hypertension, brain swelling, reduced perfusion of surviving neural tissue, reduced oxygen and metabolite delivery, reduced clearance of metabolic waste and toxins). Other examples of neuropathological conditions that may be treated include, but are not limited to, hydrocephalus, hemorrhagic stroke, brain tumors, and / or metabolic derangements. The administration of the treatment may be prompted and / or guided by the obtained measure. For example, as noted above, the system (e.g., via a display, which may include a GUI) may communicate (e.g., via text, graphic, and / or sound) an alert to the user (e.g., healthcare provider) to begin, modify, and / or cease administration of the treatment to the subject. In some embodiments, the user begins and / or continues monitoring of the ICC (e.g., alone or in combination with monitoring ICP) at least in part based on the obtained information (e.g., due to an alert triggered by the information and communicated via, for example, a display of the system). As discussed above, the monitoring may be performed continuously for at least a period of time (e.g., for at least 1 minute, at least 10 minutes, at least one hour, at least two hours, at least 5 hours, at least 12 hours, at least 24 hours, at least 48 hours, or longer). In some instances, the type, timing, and / or degree (e.g., dosage and / or duration) of treatment administered by the user is chosen based at least in part on the value of the obtained measure of ICC (e.g., alone or in combination with ICP). For example, the type, timing, and / or degree of treatment may be based at least in part on whether the value of the obtained measure of ICC exceeds or is below a threshold (e.g., a predetermined threshold and / or a threshold entered by a user). For example, the treatment type, timing, and / or degree may be affected by whether the value of the obtained ICC exceeds or is below a value that is greater than a clinically accepted threshold value. In some embodiments, the type, timing, and / or degree of treatment may be based at least in part on whether the value of the obtained measure of ICC exceeds or is below a threshold (e.g., a predetermined threshold and / or a threshold entered by a user) and whether the value of the measure of ICP exceeds or is below a threshold. For example, the treatment type, timing, and / or degree may be affected by whether the value of the obtained ICC exceeds or is below a value that is greater than a clinically accepted threshold value and whether the value of the ICP exceeds or is below a value that is #14318346v1 greater than or equal to 20 mmHg, greater than or equal to 22 mmHg, greater than or equal to 30 mmHg, greater than or equal to 35 mmHg, and / or up to 40 mmHg, up to 60 mmHg or greater. In some embodiments, the administered treatment is a treatment for controlling intracranial hypertension. In some embodiments, the treatment is based on prevailing clinical guidelines for the management of severe traumatic brain injury for the relevant population for the treated subject (e.g., adult or pediatric). Examples of treatments (e.g., for a neuropathological condition such as traumatic brain injury) that may be administered based at least in part on the value of the obtained measure include, but are not limited to body positioning (e.g., positioning of the head), osmolar therapy (e.g., hyperosmolar therapy, such as a bolus of hypertonic saline (e.g., 3%) with dosage between 2-5 mL / kg over 10-20 min., or continuous infusion at dosage of between 0.1 and 1. mL / kg per hour), decompression (e.g., decompressive craniectomy), analgesics, sedatives, neuromuscular block, seizure prophylaxis, ventilation therapy (e.g., hyperventilation), temperature control, diuretics (e.g., mannitol, for example at doses of 0.25 to 1 g / kg body weight), cerebrospinal fluid (CSF) drainage, sedation, pharmacologic paralysis, and / or barbiturates. In some embodiments, the administered treatment is chosen from one or more Tier 0 treatments (e.g., basic intensive care unit monitoring; intubation and ventilation; head elevation; analgesia and / or sedation; fever prevention, hyponatraemia prevention; CPP > 60 mm Hg; haemoglobin > 7 g / dL; and / or seizure prophylaxis), one or more Tier 1 treatments (e.g., administration of fluids and / or inotropes to increase MAP; increasing ventilation to achieve PaCO235-38 mmHg; increasing amounts of propofol, benzodiazepines, and / or opioids; administering a bolus of mannitol and / or hypertonic solutions; external ventricular drain; and / or EEG monitoring and / or phenytoin and / or levetiracetam), one or more Tier 2 treatments (administration of fluids and / or inotropes to increase MAP; increasing ventilation to achieve PaCO232-35 mmHg; and / or neuromuscular blockade administration), and / or one or more Tier 3 treatments (e.g., thiopental or pentobarbital infusion; large surgical fronto-temporo-parietal craniectomy; and / or mild hypothermia with surface and / or endovascular cooling methods). In some such embodiments the chosen Tier is based at least in part on the indication of a measure of a value ICC (in some instances in combination with an indication of a measure of a value of ICP) output by the processor. #14318346v1 For example, the alert may cause the display to communicate which Tier is appropriate for the patient at a given point in time. Further information of recommended treatments for severe brain injury (e.g., to control intracranial pressure) is provided in Carney, et al. (2017). “Guidelines for the management of severe traumatic brain injury.” Neurosurgery, 80(1), 6-15; in Kochanek, et al. (2019). “Guidelines for the management of pediatric severe traumatic brain injury: update of the brain trauma foundation guidelines.” Pediatric Critical Care Medicine, 20(3S), S1-S82; and in Zoerle, T. et al. (2024). “Intracranial pressure monitoring in adult patients with traumatic brain injury: challenges and innovations.” The Lancet Neurology, 23(9), 938-950, each which is incorporated herein by reference. An illustrative implementation of a computer system 1900 that may be used in connection with any of the embodiments of the technology described herein is shown in FIG.5A. The computer system 1900 includes one or more processors 1910 and one or more articles of manufacture that comprise non-transitory computer-readable storage media (e.g., memory 1920 and one or more non-volatile storage media 1930). The processor 1910 may control writing data to and reading data from the memory 1920 and the non-volatile storage device 1930 in any suitable manner, as the aspects of the technology described herein are not limited in this respect. To perform any of the functionality described herein, the processor 1910 may execute one or more processor- executable instructions stored in one or more non-transitory computer-readable storage media (e.g., the memory 1920), which may serve as non-transitory computer-readable storage media storing processor-executable instructions for execution by the processor 1910. Computing device 1900 may also include a network input / output (I / O) interface 1940 via which the computing device may communicate with other computing devices (e.g., over a network), and may also include one or more user I / O interfaces 1950, via which the computing device may provide output to and receive input from a user. The user I / O interfaces may include devices such as a keyboard, a mouse, a microphone, a display device (e.g., a monitor or touch screen), speakers, a camera, and / or various other types of I / O devices. The display device may be the display described above (e.g., configured to communicate the information output from the processor). #14318346v1 As noted above, the system of this disclosure may comprise one or more probes configured to perform measurements at a patient site and send information about the measurements to the at least one hardware processor (e.g., a probe configured to acquire data related to ABP, a probe configured to acquire data related to CBF, and / or a probe (e.g., an invasive probe) configured to acquire data related to ICP). FIG.5B shows an illustrative implementation of one such embodiment, where system 1000 comprises computing device 1900 (described above) and probe(s) 2000 configured to interface with computing device 1900. Probe(s) 2000 can be a single probe or multiple different probes (e.g., as described above). In some embodiments, probe(s) 2000 comprises an ultrasound probe. In some embodiments, probe 2000 comprises one or more sensors. The sensors may include single ultrasonic transducers or arrays thereof (e.g., capacitive micromachined ultrasonic transducers (CMUTs) or piezoelectric ultrasonic transducers, among other ultrasonic transducers), and / or sensors other than ultrasonic transducers. In some embodiments, probe(s) comprises a probe configured to obtain data identifying a measure of ICP of the patient (e.g., an invasive or non-invasive probe). The above-described embodiments can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor (e.g., a microprocessor) or collection of processors, whether provided in a single computing device or distributed among multiple computing devices. It should be appreciated that any component or collection of components that perform the functions described above can be generically considered as one or more controllers that control the above-discussed functions. The one or more controllers can be implemented in numerous ways, such as with dedicated hardware, or with general purpose hardware (e.g., one or more processors) that is programmed using microcode or software to perform the functions recited above. In this respect, it should be appreciated that one implementation of the embodiments described herein comprises at least one computer-readable storage medium (e.g., RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible, non- transitory computer-readable storage medium) encoded with a computer program (i.e., a #14318346v1 plurality of executable instructions) that, when executed on one or more processors, performs the above-discussed functions of one or more embodiments. The computer- readable medium may be transportable such that the program stored thereon can be loaded onto any computing device to implement aspects of the techniques discussed herein. In addition, it should be appreciated that the reference to a computer program which, when executed, performs any of the above-discussed functions, is not limited to an application program running on a host computer. Rather, the terms computer program and software are used herein in a generic sense to reference any type of computer code (e.g., application software, firmware, microcode, or any other form of computer instruction) that can be employed to program one or more processors to implement aspects of the techniques discussed herein. The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of processor-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the disclosure provided herein need not reside on a single computer or processor but may be distributed in a modular fashion among different computers or processors to implement various aspects of the disclosure provided herein. Processor-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments. Also, data structures may be stored in one or more non-transitory computer- readable storage media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a non-transitory computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to #14318346v1 establish relationships among information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationships among data elements. FIG.5C shows a flowchart of an example of a method for outputting in an indication of a measure of a value of ICC of the patient. Computer-readable storage medium 1930 may comprise processor-executable instructions that can be executed by processor 1910 to perform method 500 as shown in FIG.5C. Method 500 may comprise obtaining a set of data identifying (a) a measure of arterial blood pressure of a patient, (b) a measure of cerebral blood flow of the patient, and (c) a measure of intracranial pressure (ICP) of the patient (shown in block 501). Method 500 may further comprise outputting an indication, based at least in part on the set of data, of measure of a value of intracranial compliance (ICC) of the patient (shown in block 502). In summary, aspects of this disclosure relate to development of a method for ICC measurement in patients undergoing ICP monitoring (e.g., ICP monitoring). The method may allow for continuous ICC monitoring without increasing the risk of cranial infection. In an example embodiment, ultrasound measurements may be acquired using standard clinical-grade devices and may be used to determine CBF waveforms. The CBF waveform may be combined with ABP and ICP measurements to determine estimates for the ICC in a model-based, continuous fashion. The approach has been validated qualitatively in humans (invasive ICC reference measurements are not available in humans) and also quantitatively in an animal model of altered ICC. It is believed that the systems and methods of this disclosure constitutes a significant step forward towards reliable minimally-invasive neuromonitoring. Accordingly, some embodiments comprise obtaining (a) a measure of arterial blood pressure of a patient, (b) a measure of cerebral blood flow of the patient, and (c) a measure of intracranial pressure (ICP) of the patient; and generating a measure of a value of intracranial compliance (ICC) of the patient based at least in part on (a) the measure of arterial blood pressure of a patient, (b) the measure of cerebral blood flow of the patient, and (c) the measure of intracranial pressure (ICP) of the patient. The measure of a value of ICC of the patient may be generated by processing (a), (b), and (c) using a model (e.g., a physiological model and / or a statistical model). #14318346v1 Example Special Purpose Computer Systems Programmed with Statistical Models Systems and methods for providing measures of ICC may process, as training data for generating a trained statistical model, one or more of (a) at least one measure of arterial blood pressure of one or more patients, (b) at least one measure of cerebral blood flow of one or more patients, and (c) at least one measure of intracranial pressure (ICP) of one or more patients, as described throughout the disclosure. In some embodiments, systems and methods for providing measures of ICC may include a model. The model may be configured to receive, as input, one or more of (a) at least one measure of arterial blood pressure of a patient, (b) at least one measure of cerebral blood flow of the patient, and (c) at least one measure of intracranial pressure (ICP) of the patient, and provide, as output, at least one measure of ICC. In some embodiments, the model may comprise a statistical model trained on training data for known or annotated measures of ICC. Some models described herein may be trained using supervised machine learning. Supervised machine learning may include providing labeled training data to a classifier and penalizing or rewarding the classifier depending on whether the classifier correctly classifies the training data. For example, training a classifier to classify images of objects labeled as red, blue, or green may include rewarding the classifier for correctly classifying a green-labeled image of grass as green, and penalizing the classifier for incorrectly classifying a red-labeled image of a firetruck as blue. Thus, the classifier may properly classify future image inputs and infer whether to classify the images as red, blue or green. Accordingly input data may comprise labels indicating that the data is known to have similar characteristics, and / or different characteristics, in order to emphasize and / or de-emphasize the similar and / or different characteristics during training. During training of a model, weights and / or biases of the model may be adjusted to emphasize recognition of the particular characteristics of the training data. Supervised learning techniques may be useful for sorting new data into known categories, as in the image example. Some models described herein may also be trained using unsupervised machine leaning. Unsupervised machine learning may include providing unlabeled training data to an encoder which the encoder may sort into self-similar groups. For example, the same images provided to the classifier above may be provided to an #14318346v1 encoder, which may map the images into a continuous space. In this example, the encoder may form clusters of similar images based on various perceived characteristics of the images, such as the color of the object in each image. However, unlike training the classifier, the encoder may take into account other characteristics of the input data, such as the shape of the objects in the images, and the encoder is not penalized for doing so in the manner described for the classifier. Accordingly, such encoders may be configured to group future inputs based on characteristics encountered during training. In some embodiments, models described herein may be trained using machine learning techniques that combine aspects of unsupervised and supervised machine learning. In one such embodiment, a trained statistical model such as a neural network, large language model (LLM) such as a generative pre-trained transformer (GPT), or other appropriate statistical model, may be trained to output measures of ICC using one or more of (a) at least one measure of arterial blood pressure of one or more patients, (b) at least one measure of cerebral blood flow of one or more patients, and (c) at least one measure of intracranial pressure (ICP) of one or more patients described herein as input to the trained statistical model. In one embodiment, the (a) measures of arterial blood pressure of patients, (b) measures of cerebral blood flow of the patients, and (c) at least one measure of intracranial pressure (ICP) of patients, such as some or all of the measures described with respect to FIGs.1, 2, 3A-3C, 4A- 4B, 6A-6D, 7A-7D, 8A-8B, 9A-9C and 10 (or other measures), along with information identifying known measures of ICC is input as training data into a statistical model in a machine learning module. Once these inputs have been received, the machine learning module may generate a trained statistical model using the training data. The resulting output from the machine learning module may correspond to an ICC measures model, which is a trained statistical model of ICC measures as a function of some or all of the types of the (a) measures of arterial blood pressure of patients, (b) measures of cerebral blood flow of the patients, and (c) at least one measure of intracranial pressure (ICP) of patients, such as some or all of the measures described with respect to FIGs.1, 2, 3A-3C, 4A-4B, 6A-6D, 7A-7D, 8A-8B, 9A-9C and 10. The trained statistical model may also be stored in an #14318346v1 appropriate non-transitory computer readable medium for subsequent use as detailed further below. It should be understood that the trained statistical models disclosed herein may be generated using any appropriate statistical model. For example, a machine learning module may correspond to any appropriate fitting method capable of generating the desired trained statistical models. It should also be understood that the above methods may be combined with any appropriate type of fitting approximation to provide a desired combination of model accuracy versus computational expense. In general, a statistical model comprises a functional component designed and / or trained to analyze new inputs based on probabilistic patterns observed in prior training inputs. In this sense, statistical models differ from “rule-based” models, which typically apply hard-coded deterministic rules to map from inputs having particular characteristics to particular outputs. However, in some embodiments, a rule-based model is employed to process the inputs discussed in this disclosure to calculate a measure of ICC. By contrast, a statistical model may operate to determine a particular output for an input with particular characteristics by considering how often (e.g., with what probability) training inputs with those same characteristics (or similar characteristics) were associated with that particular output in the statistical model’s training data. To supply the probabilistic data that allows a statistical model to extrapolate from the tendency of particular input characteristics to be associated with particular outputs in past examples, statistical models are typically trained (or “built”) on large training corpuses with great numbers of example inputs. Typically, the example inputs may be labeled with the known outputs with which they should be associated, usually by a human labeler with expert knowledge of the domain. Characteristics of interest (known as “features”) are identified (“extracted”) from the inputs, and the statistical model learns the probabilities with which different features are associated with different outputs, based on how often training inputs with those features are associated with those outputs. When the same features are extracted from a new input (e.g., an input that has not been labeled with a known output by a human), the statistical model can then use the learned probabilities for the extracted features (as learned from the training data) to determine which output is most likely correct for the new input. #14318346v1 Various inventive concepts may be embodied as one or more processes, of which examples (for example, FIG.5C) are provided. The acts performed as part of each process may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments. U.S. Provisional Patent Application No.63 / 686,073, filed August 22, 2024, and entitled “System and Methods for Intracranial Compliance Measurement,” is incorporated herein by reference in its entirety for all purposes. The following examples are intended to illustrate certain embodiments of the present invention, but do not exemplify the full scope of the invention. EXAMPLE 1 This Example describes use of examples of physiological models for estimating ICC and validation of an example of a system of this disclosure in pediatric patients undergoing invasive ICP monitoring. Several lumped-parameter models of cerebral hemodynamics have been developed in the literature. Such models often contain multiple parameters whose values cannot be robustly identified in a patient-specific manner. Simpler two- / three-element models have also been developed in the past whose parameters can be estimated in a patient-specific manner. To-date, however, these models do not include effects due to ICC. Therefore, a three-element model of cerebral hemodynamics whose parameters can be readily identified and that simultaneously includes effects of the ICP, ICC, and CVR has been developed. FIG.6A illustrates the distributed nature of the craniospinal compartment and associated vascular network. A more simplified representation is then created as shown in FIG.6B comprising three sub-compartments – brain tissue, cerebrospinal fluid (CSF), and the vascular network. ABP drives blood through the vasculature. The blood drains out through the veins that are assumed to be in a collapsed state at the distal end because of higher external ICP. Thus, the ICP serves as the effective downstream pressure opposing blood flow. The brain tissue and CSF are surrounded by a series of meningeal layers, the outermost of which is the dura matter. The pressure-volume characteristic of the combined three-compartment system is #14318346v1illustrated in FIG. 6B and can be linearized about the mean ICP, ^ ̅i, resulting in a linearcompliance, ^i about a DC operating point, ^dc. The resulting linearized system can be represented by its electrical analogue in FIG.6D where ABP drives CBF through the cerebral vasculature. The vasculature is in turn modeled as a variable resistor, ^. The brain-tissue and vascular compliance is represented by an incremental capacitor, ^a,b, while the ICC is represented by the incremental capacitor, ^i, operating at a DC set- point, ^dc. In concordance with the Starling’s resistor phenomenon, the outflow (venous) pressure is modeled as a dependent pressure source, equal to the ICP. As described in the below, a frequency-domain approach has been developed that uses ABP and CBF waveforms as input to the model of this Example and determinesbest-fit estimates for ^, ^i, ^a,b, and the mean ICP, ^ ̅i in a patient-specific manner.Estimation was performed in non-overlapping windows of 60-seconds where the model- parameters were assumed to be invariant over time. Importantly, all parameters were extracted using measured data, and it was ensured that hyperparameters, where necessary, were physiologically interpretable. This rendered the estimates largely patient-specific, distinguishing this example approach from conventional learning-based schemes. Validation in a Rabbit Model A rabbit model of raised ICP was set up to investigate (a) the fidelity of the proposed three-element model, as well as (b) to validate the ensuing estimates of ICP, ICC, and CVR. The animal model was necessitated because, first, ground-truth ICC and CVR measurements are not available in contemporary neurocritical care environments rendering validation of model-based estimates impossible. Also, due to the high standard of care in modern neurocritical care units, the majority of patients have ICP values in the normal range (≤ 20 mmHg), making it difficult to validate ICP (and ICC / CVR) estimation performance in pathologically severe conditions. Adult New Zealand White rabbits (weighing 3–5 kg) were instrumented with a single-lead ECG. Blood pressure measurements were acquired (unilaterally) at the common carotid artery. Femoral arterial blood pressure was also acquired. An ultrasonic flow-probe was used to acquire blood flow going to the cerebral vasculature. An intraparenchymal ICP sensor was used to measure the ICP. Two balloons (pediatric #14318346v1 Swan-Ganz catheters) were also placed epidurally. The first balloon, B1, was connected to a computer-controlled syringe pump and was used to modulate the ICP. The second balloon, B2, was connected to a custom-designed ECG-gated pneumatic syringe pump. The pump was designed to rapidly inflate the second ballon by a small volume (~0.1 mL) to independently calculate the ICC. Specifically, the second ballon was inflated intermittently in an ECG-gated manner and the resulting change in ICP was recorded. The ICC was then calculated by dividing the known inflation volume with the recorded ICP changes. The measured ABP and CBF data are illustrated for one rabbit in Fig.7A and FIG.7B respectively. The inflation profile for the first ballon along with the resulting change in ICP is illustrated in FIG.7C while the ICC measurements acquired during this period are shown in FIG.7D. The reference CVR was calculated by using the measured ABP, ICP, and CBF and is shown in Fig.7D. Data from three rabbits was used to help ensure repeatability. FIGS.8A-8B show the PV characteristic obtained for the data shown in Fig.7C. Data are shown both for stepped and continuous inflation / deflation profiles of the first balloon. The path followed during balloon inflation is markedly different from the profile during subsequent balloon deflation. This, together with the overall nonlinear PV response demonstrates the importance of joint ICP-ICC monitoring. Known values of the ICP along with the ABP and CBF were fed into the model and the ICC alone was estimated. This helped evaluate the model’s ability to represent changes in ICC. The results are shown in FIGS.9A-9C for each of the three studies. The model-based ICC estimates can be seen to follow the ground-truth measurements as the ICP goes through large changes. A Bland-Altman plot for the estimates is shown in FIG. 10. Overall, in 10660-second non-overlapping windows where reference invasive ICC measurements were available, the ICC estimates had a mean estimation bias of 0.002 mL / mmHg and a mean absolute error (MAE) of 0.007 mL / mmHg, with reference ICC ranging from 0.004 mL / mmHg to 0.061 mL / mmHg (IQR: 0.007–0.027 mL / mmHg). The results clearly demonstrate the ability of the three-element model to represent changes the ICC. As discussed above in this disclosure, such ICC estimates can be useful in patients already undergoing invasive ICP measurements, allowing continuous ICC monitoring in absolute physiological units of mL / mmHg without the need to carry out time- and resource-intensive infusion tests. #14318346v1 Discussion Rabbit models have routinely been used as a surrogate for experiments on the human cerebral vasculature. While rabbits have a small ventricular space, these experiments did not involve fluid injection or withdrawal in the ventricles, and hence rabbit models were suitable for this work. The comparatively thin rabbit skulls made intracranial instrumentation relatively simple and highly repeatable, leading to high- fidelity and reliable reference measurements of the ICP, ICC, and CVR. With the proposed experimental protocol, cerebral arterial blood pressure and cerebral blood flow were able to be measured despite the small size of the rabbit vasculature. It is believed that this makes this study distinctive in making available not just invasive ICP but also ICC and CVR measurements simultaneously for comparison against corresponding estimates. Using the data from the rabbit study, known ICP and CVR values were used to successfully determine the ICC in a continuous, fully patient-specific manner. Such estimation is important because for the first time, continuous monitoring of ICC inside neuro-critical care units (NCCUs) is practical without the need for specially-designed indwelling systems such as the Spiegelberg device or infusion of a control volume into the cerebrospinal fluid space. Specifically, patients undergoing invasive ICP monitoring in an NCCU typically also undergo continuous ABP monitoring. Thus, only with the addition of one relatively simple and fully noninvasive monitoring device (ultrasound- based CBF), continuous ICC can be made available for use by clinicians. That the results in FIGS.9A-9C and FIG.10 were acquired in a completely training-free manner via a simple and interpretable model makes the approach more appealing and computationally tractable to provide estimates (e.g., in real-time at, for example, the patient’s bedside). While surrogates for continuous ICC measurement have been proposed in the past, these surrogates have not found widespread adoption partly because the measurements are not in absolute units (e.g., of mL / mmHg). The system and methods of this disclosure are distinctive in that they can yields ICC in the requisite units of volume divided by pressure (e.g., mL / mmHg). This is facilitated in this Example by the use of volumetric CBF, as well as the two-capacitor physiological model that yields both ICC as well as cerebral arterial (and brain) compliance. It is believed that such functionality has #14318346v1 not been demonstrated in the past. Having ICC in units of volume divided by pressure (e.g., mL / mmHg) is important because it facilitates long-term inter- and intra-subject comparisons, paving the way for the use of ICC as a reliable and actionable diagnostic biomarker. Methods Mathematical Model of Cerebral Hemodynamics The following is a mathematical description of the system in FIG.6D. For a data window of suitable length (~60 s) it is assumed that the capacitors, ^a,band ^i, and vascular resistance, ^, have static values, i.e., their values do not vary within the data window. Then, the circuit dynamics may be expressed as ( 1 ) Mean-subtracted quantities are denoted with an overhead tilde. For the voltage-divider formed by the two capacitors, one may then write ( 2 ) where ^ = ^a,b / (^a,b + ^i). The relationship in ( 2 ) suggests that within the confines of this lumped-parameter model, the mean-subtracted ICP and ABP waveforms are proportional to each other. Expressing ( 1 ) for mean-subtracted quantities and substituting the expression in ( 2 ), ( 3 ) #14318346v1 where pc̃(^) = pã(^) – pĩ(^) is the mean-subtracted cerebral perfusion pressure (CPP) waveform. Equation ( 3 ) expresses time-domain representations of this model of cerebral hemodynamics. These representations are then used to determine the ICC using known ICP. In applications where invasive ICP measurements are available and continuous estimates of ICC are desired, an estimate for the constant ^ in a least-square error sense is determined according to ( 4 ) The remainder of the estimation routine is formulated using the power spectral density (PSD) of the measured ABP and CBF. Using PSDs increases the robustness against temporal misalignment of CBF and ABP waveforms, as may commonly occur due to different measurement devices. Expressed in terms of PSDs, ( 3 ) may be written as ( 5 ) where ( is the frequency in Hertz, and |Q'^^(^|$and |^^^^(^|$are the PSDs of the (mean- subtracted) CBF and CPP, respectively. Using the known value for mean ICP, the CVR may be estimated as ^̅ − ^̅^* = ^ ^q-^( 6 ) where the overhead bars indicate mean quantities. Then, a least-square-error estimate is found for ^a,b according to ^.$^,^ = arg #14318346v1 The ICC, ^i, is then estimated according to ( 8 ) In practice, a finite number of frequencies, (, is used for the estimation in this Example. Typically, only frequencies at the heart / respiratory rate, and their harmonics are used. Rabbit Model of Raised ICP Rabbit models have been used previously in investigations related to the ICP. For instance, Z. Feldman, et al. "Positive end expiratory pressure reduces intracranial compliance in the rabbit," Journal of Neurosurgical Anesthesiology, vol.9, no.2, pp. 175-179, 1997 developed an experimental procedure to obtain craniospinal pressure-volume relationships in rabbits. A pediatric Swan-Ganz balloon catheter was surgically placed over the dura and was inflated to modulate the ICP. The embodiments in this Example employ experimental protocol that build on such work, but differs in two crucial aspects: this Example did not just modulate and measured the ICP, but also simultaneously acquired the ICC using independent means; and this Example simultaneously captured cerebral hemodynamic information through recordings of the ABP and CBF waveforms. The experimental protocol in this Example was approved by the Massachusetts Institute of Technology (MIT) Committee on Animal Care. Experiments were conducted on adult New Zealand White rabbits weighing 3–5 kg. All experiments were performed under veterinary guidance at a facility managed by the MIT Division of Comparative Medicine. Animals were brought for acclimatization to the facility at least 72 hours prior to an experiment. Anesthesia induction and physiological monitoring All experiments were performed under anesthesia. To induce anesthesia initially, a nose cone was placed over the rabbit and connected to 2–4% isoflurane- oxygen gas mixture (oxygen delivery set between 0.5–1 L / min). Once the animal was anesthetized, an endotracheal tube was inserted, and was connected to the #14318346v1 anesthesia machine with the isoflurane concentration set between 2–3%. Next, a photoplethysmogram signal was obtained at a toe. A rectal thermometer was inserted to monitor core body temperature and single-lead (Lead I) ECG was also acquired and amplified (BioAmp, ADInstruments, Dunedin, New Zealand). A capnogram signal was also acquired to monitor end-tidal carbon-dioxide and the respiratory rate. Depth of anesthesia was subsequently monitored using pain reflexes and the isoflurane concentration was adjusted appropriately under veterinary guidance. ICP measurement and modulation The animal was placed in a prone position to allow access to the skull. A midline skin incision was made to expose the sagittal suture landmark. Bilateral burr holes were then drilled into the parietal lobes. A 3.5 Fr diameter intraparenchymal ICP sensor (Millar, Houston, TX, USA) and a balloon catheter (pediatric Swan- Ganz, 5Fr diameter) were inserted in the two burr holes, respectively. The balloon catheter was placed epidurally and the drill-sites were sealed via dental cement (MasterMedi, Gurgaon, HR, India). Air was evacuated from the balloon catheter and it was subsequently connected to a water-filled syringe (water is nearly incompressible in the pressure ranges encountered here). The syringe was driven by a computer-controlled precision syringe pump (PhD Ultra, Harvard Apparatus, Holliston, MA, USA) and was used to alter the ICP through controlled infusions. ICC measurement ICC measurements have been reported to vary as a function of cardiac cycle, and ICC measurements must therefore be ECG-gated. Rabbits have heart rates often exceeding 200 bpm, and thus any volume-infusion-based ICC measurement must carry out the volume infusions very rapidly (50–150 ms) in order to achieve such ECG-gated measurements. A pneumatic-powered syringe pump was developed in- house for this purpose. A pneumatic spring-loaded piston (McMaster Carr, Elmhurst, IL, USA) was connected to a custom-machined assembly. A water-filled syringe was fixed to the assembly. The system was designed such that the syringe plunger could be pushed forward in fixed increments (2 mm). The syringe was connected to a second balloon catheter that was also placed epidurally via a burr hole. The drill site #14318346v1 was sealed as before. The single-lead ECG was fed to a microcontroller (STM32F407, STMicroelectronics, Plan-les-Ouates, Switzerland). A real- time QRS detector was programmed and was used to drive a solenoid valve connecting the piston to a pressurized (60 psi) air tank. The valve was turned on to drive the piston and infuse a fixed volume of water (~50 mL) into the balloon. A second valve was used to subsequently depressurize the piston. The procedure was repeated for a range of cardiac-cycle phases. Compliance calculations were performed offline using the corresponding ICP measurements. For each syringe infusion, the change in ICP was determined and the ICC was calculated according to ΔV ICC = ΔICP + ^ ( 9 ) where ΔV was the infused balloon volume, ΔICP was the resulting change in ICP, and 1 was a small constant, set to 0.5 mmHg to prevent division by very small numbers in cases of high ICC. The compliance measurements across cardiac cycles were averaged to yield a representative value. In subsequent description, the balloons used for ICP modulation and ICC measurement will be referred to as B1 and B2, respectively. Arterial blood pressure and cerebral blood flow measurement With the intracranial instrumentation in place, the rabbit was moved to a supine position. Blunt dissection was carried out along the ventral midline to access the common and internal carotid arteries. Subcutaneous lidocaine (2%) at 2–4 mg / kg was used just prior to making the incisions. A 24G catheter was introduced unilaterally in one common carotid artery and was connected to an external blood pressure sensor. The distal end of the artery was ligated to prevent the catheter from getting dislodged. An ultrasonic flowprobe (Transonic Systems, Ithaca, NY, USA) was placed along the contralateral common carotid artery. The corresponding external carotid was clamped and thus the measured flow corresponded to that going #14318346v1 through the corresponding internal carotid artery. Femoral incision was also carried out unilaterally and a blood pressure transducer (2 Fr, Millar) was inserted to acquire femoral arterial blood pressure. Thus, two blood pressure measurements (femoral and carotid) were acquired and one flow measurement (internal carotid) was recorded. Since the internal carotid arteries are the largest vessels that supply blood to the brain, and since the contralateral internal carotid was ligated, the acquired flow waveform was considered the total CBF and flow through any smaller arteries was ignored. Constant-rate infusion (CRI) anesthesia Isoflurane-based anesthesia has been reported to affect cerebral hemodynamics . Thus, as an alternative, after the surgical procedures had been completed under isoflurane, we used CRI of ketamine at 350 μg / kg / min and dexmedetomidine at 1.6 μg / kg / min via an ear vein catheter. The infusion rate was adjusted under veterinary guidance to maintain a stable plain of anesthesia. Data acquisition Data streams were recorded on a common time axis at 1000 samples / s (PowerLab, ADInstruments, Dunedin, New Zealand). The heights of the femoral and carotid ABP transducers were noted and a static offset was applied to bring the measurements at the same level as the ICP measurements, thus removing hydrostatic offsets in the pressure signals. Mean-subtracted ABP, CBF, and ICP waveforms were passed through bandpass filtering (0.1–20 Hz) and the mean values were restored after filtering. ICP manipulation Once a stable baseline had been acquired (~10 minutes), B1 was inflated in steps (0.25–1.0 mL at 3 mL / min) to raise the mean ICP to approximately 40 mmHg (See Fig.2(d)). Each inflation level was maintained for about 5 minutes before moving to a higher level. ECG-gated ICC measurements were acquired using B2 prior to and just a er each volume increment of B1. B1 was subsequently deflated in small increments and ICC measurements were repeated as before. A er a rest period, #14318346v1 B1 was inflated to a larger volume in a slow, continuous manner (~1 cc at 0.03 mL / min). This was followed by deflation in a similar, slow and continuous manner. Such inflation amounts to an acute tumor / hematoma growth model. The procedure was repeated when possible. Upon completion of data recording, the rabbits were euthanized via intravenous injection of Pentobarbital (Fatal Plus) (100 mg / kg). Data in the three studies presented in this Example were acquired with ABP measurements at the common carotid and femoral arteries, and with CBF measured through the ICA. ICC estimation with known ICP Data were analyzed offline using custom software written in MATLAB (Mathworks, Natick, MA, USA). Non-overlapping 60-second windows were considered. In each window, the respiratory and heart rates were determined using power spectral density analysis (Welch spectrograms were used). ICC estimation was performed using the least-square-error optimization criterion as described earlier. PSDs at five frequencies (221, 222, 223, ℎ21, ℎ22) were used where 22 and ℎ2 represent the respiratory and heart rates, respectively, and the subscripts indicate the respective frequency harmonic. While several embodiments of the present invention have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and / or structures for performing the functions and / or obtaining the results and / or one or more of the advantages described herein, and each of such variations and / or modifications is deemed to be within the scope of the present invention. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and / or configurations will depend upon the specific application or applications for which the teachings of the present invention is / are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and #14318346v1 equivalents thereto, the invention may be practiced otherwise than as specifically described and claimed. The present invention is directed to each individual feature, system, article, material, and / or method described herein. In addition, any combination of two or more such features, systems, articles, materials, and / or methods, if such features, systems, articles, materials, and / or methods are not mutually inconsistent, is included within the scope of the present invention. As used herein in the specification and in the claims, the phrase “at least a portion” means some or all. The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.” The phrase “and / or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Other elements may optionally be present other than the elements specifically identified by the “and / or” clause, whether related or unrelated to those elements specifically identified unless clearly indicated to the contrary. Thus, as a non-limiting example, a reference to “A and / or B,” when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A without B (optionally including elements other than B); in another embodiment, to B without A (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc. As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and / or” as defined above. For example, when separating items in a list, “or” or “and / or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of” or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law. #14318346v1 As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and / or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc. Some embodiments may be embodied as a method, of which various examples have been described. The acts performed as part of the methods may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include different (e.g., more or less) acts than those that are described, and / or that may involve performing some acts simultaneously, even though the acts are shown as being performed sequentially in the embodiments specifically described above. Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements. In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” and the like are to be understood to be open-ended, i.e., to mean including but not limited #14318346v1 to. Only the transitional phrases “consisting of” and “consisting essentially of” shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures, Section 2111.03. #14318346v1

Claims

CLAIMS What is claimed is:

1. A system comprising: at least one hardware processor; and at least one non-transitory computer-readable storage medium storing processor- executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform: obtaining a set of data identifying (a) a measure of arterial blood pressure of a patient, (b) a measure of cerebral blood flow of the patient, and (c) a measure of intracranial pressure (ICP) of the patient; and outputting an indication, based at least in part on the set of data, of a measure of a value of intracranial compliance (ICC) of the patient.

2. The system of claim 1, wherein the measure of cerebral blood flow comprises a measure of volumetric cerebral blood flow.

3. The system of any one of claims 1-2, wherein the indication output by the at least one hardware processor is of a measure of a value in absolute units of ICC.

4. The system of claim 3, wherein the measure of a value in absolute units of ICC is in the units of mL / mmHg, mL / pascal, mL / bar, mL / PSI, mL / atmosphere, or a multiple thereof.

5. The system of any one of claims 1-4, wherein the value of ICC is calculated by feeding (a) an arterial blood pressure waveform, (b) a volumetric cerebral blood flow waveform, and (c) data identifying a measure of ICP to a model.

6. The system of any one of claims 1-5, wherein the value of ICC is calculated by feeding (a) an arterial blood pressure waveform, (b) a volumetric cerebral blood flow waveform, and (c) data identifying a measure of ICP to a physiologic model. #14318346v17. The system of any one of claims 1-4, wherein the value of ICC is calculated by feeding (a) an arterial blood pressure waveform, (b) a volumetric cerebral blood flow waveform, and (c) data identifying a measure of ICP to a statistical model.

8. The system of any one of claims 1-5, wherein at least a portion of the set of data that identifies the measure of ICP of the patient is obtained from an invasive measurement of ICP on the patient.

9. The system of claim 8, wherein the invasive measurement of ICP comprises placement of a probe in the intradural space of the patient.

10. The system of any one of claims 1-5, wherein at least a portion of the set of data that identifies the measure of ICP of the patient is obtained from a non-invasive measurement of ICP on the patient.

11. The system of any one of claims 1-10, further comprising one or more probes configured to perform measurements at one or more patient sites and send information about the measurements to the at least one hardware processor, wherein at least a portion of the set of data that identifies (a) a measure of arterial blood pressure and / or (b) a measure of cerebral blood flow of the patient is based at least in part on the information about the measurements at the one or more patient sites.

12. The system of claim 11, wherein the one or more probes comprise an ultrasound probe.

13. The system of any one of claims 1-12, further comprising one or more invasive probes configured to perform invasive measurements at one or more patient sites and send information about the invasive measurements to the at least one hardware processor, wherein at least a portion of the set of data that identifies a measure of ICP of the patient is based at least in part on the information about the invasive measurements at the one or more patient sites. #14318346v114. The system of claim 13, wherein the one or more invasive probes are configured to be placed intradurally in the patient.

15. The system of any one of claims 1-14, further comprising a display configured to communicate to at least one user the indication output from the at least one hardware processor.

16. The system of claim 15, wherein the instructions, when executed by the at least one hardware processor, further cause the at least one hardware processor to perform: generating an alert regarding the administration of a treatment for relieving a neuropathological condition based at least in part on the indication output from the at least one hardware processor; and transmitting the alert to the display such that the alert triggers the display to communicate the alert to the at least one user.

17. A method, comprising obtaining a measure of intracranial compliance (ICC) using the system of any one of claims 1-16.

18. A method, comprising administering a treatment for relieving a neuropathological condition to a subject based at least in part on a measure of intracranial compliance (ICC) obtained using the system of any one of claims 1-16.

19. At least one non-transitory computer-readable storage medium storing processor- executable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to perform: obtaining a set of data identifying (a) a measure of arterial blood pressure of a patient, (b) a measure of cerebral blood flow of the patient, and (c) a measure of intracranial pressure (ICP) of the patient; and outputting an indication, based at least in part on the set of data, of a measure of a value of intracranial compliance (ICC) of the patient.

20. A method, comprising: #14318346v1obtaining a set of data identifying (a) a measure of arterial blood pressure of a patient, (b) a measure of cerebral blood flow of the patient, and (c) a measure of intracranial pressure (ICP) of the patient; and outputting an indication, based at least in part on the set of data, of a measure of a value of intracranial compliance (ICC) of the patient. #14318346v1

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