Transcranial doppler (TCD) allows the study of the hemodynamics of the cerebral vessels. The first report was documented in 1982 by Aslid et al.13 Technical improvements led to the capacity to analyze cerebral parenchyma in addition to the color visualization of the basal venous and arterial vessels and doppler spectral analysis, providing a noninvasive, bedside examination using transcranial color-coded duplex (TCCD) protocols.14,15 TCCD is usually performed using a 2–2.5 MHz probe15 and remains the most widely used nonivasive technique at bedside.16

In its basic form, the bidimensional mode allows the evaluation of midline shift and the size of the ventricular system.

To evaluate midline shift (MLS), the examination is performed from the temporal window, placing the ultrasound probe perpendicular to the walls of the third ventricle (3V), which appears as a double hyperechoic line. Then, we measure the distance from the transducer to the center of the 3V bilaterally (distances A and B). The following formula is applied: MLS = A–B/2. Whilst this can be applied to all ABI patients, it is specially useful in the follow-up of patients with malignant middle cerebral artery (MCA) stroke.17,18

The 3V appears in the diencephalic plane as a double hyperechoic line. The frontal horns of the lateral ventricles are then visualized after angulating the probe cephalad 10 degrees. Studies found an excellent correlation between computed tomography (CT) and TCCD in measuring the diameter of the 3V and the frontal horns,19,20 allowing to assess the response to cerebrospinal fluid (CSF) drainage.21

Using the doppler spectra analysis the main indications of TCCD are listed as follows:

Cerebral circulatory arrest

Cerebral circulatory arrest (CCA) occurs as a final process in patients with devastating brain injury and intracranial hypertension. Therefore, the sonographic patterns follow the evolutive changes of the increase in intracranial pressure (ICP), characterized by in crescendo high resistance patterns until the definitive cessation of CBF occurs. It is necessary to evaluate both MCA and the basilar artery (BA) in two examinations. A recent meta-analysis showed that transcranial sonography presented a sensitivity of 90% and a specificity of 98% in diagnosing CCA.25

The following evolutive patterns suggesting CCA are:

• a)

  Reverberant or oscillating flow, represented by a sonogram exhibiting anterograde systolic flow with retrograde or inverted diastolic flow.

• b)

  Systolic spikes, represented by short systolic waves (<200 ms) with low velocity (systolic peak < 50 cm/s) in the absence of any diastolic flow.

• c)

  Lack of acoustic signal, which can be accepted only when a prior TCD/TCCD has previoulsy shown adequate acoustic window and preserved flow of the basal arteries.

Screening of intracranial hypertension

The use of transcranial ultrasound in the screening of intracranial hypertension (ICH) is particularly interesting in situations characterized by limited availability of invasive monitoring and as screening to select which neurocritical care patients really necessitates invasive monitoring.9 Of note, the recent Brussels consensus stated that a multimodal approach including different noninvasive ICP methods (at least 2 different modalities) is necessary compared to a single method for ICP assessment.9 It can be evaluated in different ways.

Measurement of the diameter of the optic nerve sheath (ONSD)

The optic nerve is covered by the meninges, as an extension of the cerebral subarachnoid and subdural space. Thus, when ICP increases, the cerebrospinal fluid displaces towards this space, increasing the diameter of the optic nerve particularly in the bulbous portion, which is more distensible and is located 3 mm from the retina and the optic disc.3,14,15 This is the point where the ONSD must be measured (Fig. 1). Criteria for a high-quality and standardized examination have been recently developed through an international consensus.26

Figure 1.

Measurement of the diameter of the optic nerve sheath to rule out significant intracranial hypertension. The patient had a ONSD of 6.1 mm and ICP 25 mmHg at the time the examination was performed.

A recent meta-analysis including five hundred and forty-eight adult patients and 120 children in 16 eligible studies showed that pooled sensitivity and specificity of ONSD measurement by ultrasonography were 84% (95% CI 76%–89%) and 83% (95% CI 73%–90%), respectively.27 Pooled area under the curve was 0.91 for adults and 0.76 for children. Optimal threshold for estimating ICH was 5.76 mm for adults and 5.78 mm for children.27 The recent Brussels consensus recommended considering a threshold of 6 mm as a potential marker of ICH or excluding it (Strong Recommendation).9

Intracranial pressure according to cerebral hemodynamics

The pulsatility index (PI) has been the single most widely used hemodynamic parameter for evaluating the existence of ICH, since the circulatory flow resistances increase with rising ICP. However, PI depends on multiple physiological variables and its ability to determine increases of ICP is therefore limited.15 The Brussels consensus recommended that to raise/exclude the suspiction of ICH, a change from basal level of PI of at least 0.5 should be considered as a sign of cerebral blood flow modification potentially related to ICP shift (Strong Recommendation).9

Recently, the multicenter IMPRESSIT-2 trial analyzed the estimation of ICP through insonation of the MCA in 262 neurocritical patients using the following formula and comparing it against the invasive measurement28:

ICPtcd = MABP – CPPtcd; where CPPtcd = MABP × Vd/Vm + 14,

being: ICPtcd: transcranial ultrasound measurement of intracranial pressure; MABP: mean arterial blood pressure; CPPtcd: transcranial ultrasound measurement of cerebral perfusion pressure; Vd/Vm: diastolic/mean flow velocity in the middle cerebral artery.

The negative predictive value was elevated (ICP > 20 mmHg = 91.3%, >22 mmHg = 95.6%, >25 mmHg = 98.6%), indicating high discriminant accuracy of ICPtcd in excluding ICH. Concordance correlation between ICPtcd and invasive ICP was 33.3% (95% CI 25.6%–40.5%), and Bland-Altman showed a mean bias of −3.3 mmHg.28

Brain tissue is considered one of the most sensitive to hypoxic insults, with hypoxia representing a key mechanism of secondary brain damage. However, real-world data in our environment showed that cerebral oxygenation monitoring remains infrequently used in the neurocritical care setting.29 Near-infrared spectroscopy (NIRS) technology offers numerous advantages, including availability, potential early implementation, the capability to monitor multiple brain regions, and the absence of procedure-related complications.30 The physical principles underlying NIRS rely on the presence of specific substances known as chromophores, which absorb light within a defined wavelength spectrum (700–1300 nm, near infrared light). Blood contains a variety of these chromophores, notably oxyhemoglobin and deoxyhemoglobin, which absorb light in the near infrared range.30 By applying the Beer-Lambert law, NIRS devices can estimate the proportion of oxyhemoglobin and deoxyhemoglobin within the targeted cerebral tissue. Key parameters derived from this technology include the regional oxygen saturation (rSO₂) and the tissue oxygenation index (TOI), both of which reflect an estimation of the percentage of oxygenated hemoglobin. The rSO₂ is calculated as follows:

The most commonly used configuration involves two adhesive patches containing both emisors and detectors, typically placed on the skin over the frontal lobes. It is important to note that NIRS cannot distinguish between different vascular compartments, providing a mixed measurement of arterial, venous, and capillary saturations. Given that the majority of cerebral blood volume is in the venous compartment, its oxygen saturation is the predominant contributor to the rSO₂ value. Normal rSO₂ value is considered to range from 60% to 75%, although significant variability exists. Emphasis should be placed on monitoring trends rather than interpreting isolated values. Thresholds such as rSO₂ < 55% or a 10%–20% decrease from baseline have been associated with poorer outcomes in patients with ABI.31 Detector placement is also critical, particularly in the presence of suspected intracranial lesions such as pneumocephalus, fractures or hematomas. In traumatic brain injury (TBI), NIRS has demonstrated utility in the detection of intracranial hematomas.32 Additionally, it has shown potential for optimizing CPP and evaluating cerebral autoregulation. Some studies have demonstrated good sensitivity for detecting severe desaturation episodes (brain tissue O₂ < 12 mmHg), but its performance is less reliable for moderate events 33. In aSAH, a recent study reported a good correlation between the extent of hemorrhage and the frequency and duration of cerebral desaturation episodes (rSO₂ < 60%).34 Additionally, NIRS could be useful for detection of delayed ischemia and maintenance of CPP in aSAH patients.35

However, NIRS presents limitations.36 First, extracranial blood contamination has been documented as a potential source of measurement error. Second, no absolute treatment thresholds have been established, and inter- and intra-individual variability remains significant. Moreover, it provides data from only a few centimeters of cortical tissue. Finally, NIRS monitoring has not been consistently correlated with improved patient outcomes.

Automated quantitative pupillometry allows for objective and accurate testing of the pupil light responses. Pupillometer devices deliver a precisely defined light stimulus and capture different variables such as maximum size, minimum size, constriction velocity, constriction amplitude, and response latency.2 Dynamic pupil variables can be integrated in algorithm-derived indices such as the Neurological Pupil index (NPI).2 It can be considered a validated monitor of brain function.3

To date, the most relevant study in patients with ABI was the ORANGE, a prospective, observational cohort study conducted at 13 hospitals in eight countries in Europe and North America.37 The ORANGE included 514 patients (224 with TBI, 139 with aSAH and 151 with intracerebral hemorrhage). The 6-month outcome was assessed in 497 (97%) patients, of whom 160 (32%) patients died, and 241 (47%) patients had at least one recording of abnormal NPi, which was associated with poor neurological outcome (for each 10% increase in the frequency of abnormal NPi, adjusted odds ratio 1·42 [95% CI 1.27–1.64]; P < ·0001) and in-hospital mortality (adjusted hazard ratio 5·58 [95% CI 3.92–7.95]; P < ·0001). Therefore, automatic, repeated automated pupillometry assessment could improve the continuous monitoring of ABI progression and the dynamics of outcome prediction.37

However, despite initial enthusiasm,38,39 the role of automated pupillometry in the noninvasive prediction of ICH was evaluated in a secondary analysis of the ORANGE cohort.40 This secondary analysis included 318 adult patients who required intensive care unit admission, intubation, and mechanical ventilatory support due to acute TBI (n = 133 [41.8%]), intracerebral hemorrhage (n = 104 [32.7%]), or aSAH (n = 81 [25.5%]) and had automatic pupillometry used as part of the standard evaluation practice and ICP monitoring.39 No significant association between ICP and abnormal NPi (<3; odds ratio, 1.01; 95% CI, 0.99–1.03) or absent NPi (0; odds ratio, 1.03; 95%CI 0.99–1.06) was observed.40 Therefore, in that heterogeneous population of ABI patients, the NPi values were not significantly associated with ICP values and repeated NPi measurements may not be a sufficient replacement for invasive monitoring.40 However, the recent Brussels consensus recommended a NPi < 3 to be used (in combination with at least another tool) to suspect the presence of elevated ICP, and NPi ≥ 4 to rule out the presence of elevated ICP.9

Additionally, automated pupillometry and specifically, the NPI proved to be a promising tool in the screening of delirium,41 need of intense neurocritical care,42 and identifying covert consciousness.43 More established is its role in the multimodal approach to neuroprognostication after cardiac arrest.44 In a recent validation study including 710 survivors after out-of-hospital cardiac arrest, a NPi ≤ 2 predicted outcome with 0% false-positive rate (FPR) at all time points (0−72 h), and qPLR < 4% at 24−72 h. In patients with motor GCS ≤ 3 at ≥72 h, pupillometry thresholds significantly increased the sensitivity of neuron-specific enolase, from 42% (35%–51%) to 55% (47%–63%) for qPLR and 50% (42%–58%) for NPi, maintaining 0% (0%−0%) FPR.45

The electroencephalogram (EEG) is a pivotal tool in neurocritical care, utilized primarily to assess cerebral electrical activity, detect seizures, and monitor brain function in critically ill patients.46 EEG records spontaneous electrical activity generated by cortical neurons through electrodes placed on the scalp. It provides real-time information on neuronal function, enabling early detection of subtle brain dysfunctions, especially non-convulsive seizures or status epilepticus (SE).2,3,46,47 Potential applications of EEG in neurocritical care are summarized in Table 1.

Continuous EEG (cEEG) monitoring has gained importance due to its sensitivity in detecting electroencephalographic abnormalities, which may remain undetected with intermittent EEG assessments.48 However, the implementation of cEEG remains inconsistent. A Canadian multicentre observational study involving medical, surgical, trauma, and neurological ICUs revealed that out of 375 screened patients, 34% met the criteria for cEEG monitoring recommended by the European Society of Intensive Care Medicine. Yet, 63% of those patients did not receive cEEG monitoring. This included 20% of SE patients without return to baseline, 67% of intracerebral hemorrhage patients with altered consciousness, and 100% of patients undergoing targeted temperature management after cardiac arrest.49 In addition, cEEG has been associated with reduced in-hospital mortality among mechanically ventilated patients and critically ill hospitalized patients.50 Indeed, in patients with suspected nonconvulsive SE, initial cEEG may be necessary to support the diagnosis by identifying early sporadic epileptiform discharges and early rhythmic or periodic EEG patterns of “ictal-interictal uncertainty”.51

Simplified quantitative methods such as the Bispectral Index Scale (BIS) have emerged as a form of processed EEG (pEEG). BIS, initially developed in the field of anesthesiology, processes raw EEG signals through advanced algorithms analyzing amplitude, frequency, and bispectral characteristics to generate a numeric value from 0 (isoelectric EEG) to 100 (fully awake) reflecting depth of sedation or consciousness.52 BIS and others pEEG indexes have extended into neurocritical care as a surrogate for sedation adequacy, depth of coma, or prognosis after ABI. A recent expert consensus guideline recommended the use of pEEG to monitor the level of sedation (strong consensus), and it was recommended that all sedated patients (paralyzed or nonparalyzed) unfit for clinical evaluation would benefit from depth of sedation monitoring (strong consensus).53

Despite its utility, EEG presents several limitations. Interpretation requires significant expertise, often unavailable outside specialized neurological units. EEG recordings are frequently complicated by technical artifacts from muscular activity, sedation-induced alterations, patient movement, and interference from ICU equipment. Furthermore, EEG data provide a qualitative rather than quantitative analysis, complicating standardization of interpretation and clinical decision-making.47 This underscores the need for index-based EEG methodologies to be implemented only in the populations and settings in which were validated. In this context, point-of-care EEG systems, specifically utilizing automated computational algorithms for quantitative EEG analysis have facilitated bedside visualization for targeting timely clinical interventions by ICU staff, replacing the need for raw EEG interpretation.54,55

ICP waveforms (ICPW) are the representation of pulse transmission of arterial pressure via the choroid plexuses to the CSF and brain parenchyma.56,57 The standard ICPW has three components: a) P1 “percussion wave” represents the arterial input; b) P2 “tidal wave”; represents cerebral parenchyma and P3 or “dicrotic wave”, is a reflect of venous outflow.56,57 P1 has a sharp peak and in general its amplitude remains constant. P2 component is more variable in shape and amplitude. Usually after P3, the pressure wave decreases to its diastolic position.

Compliance refers to the ability of a mechanical system to respond to an applied force, expressed as the reciprocal of the system’s stiffness.58,59 In steady status inside of the cranial cavity, the ICPW conserve its basic morphology where P1 > P2 > P3 (stage 1 of pressure/volume curve). When the intracranial content increases and compensatory mechanisms are still preserved, the ICP “number” does not increase, but the waveform presents an increase in the P2 component compared to P1 (stage 2, compliance compromise without changes in ICP values)58,59 (Fig. 2).

Figure 2.

Noninvasive analysis of intracranial pressure waveforms.

On the other hand, ICP pulse amplitude (difference between systolic and diastolic values) also tends to increase.58,59 Only when the compensation capacity is exhausted, the ICP “number” increases and its waveform adopt a pyramidal shape with a gradual disappearance of P1, P3 and the marked ascent of P2.58,59 Physiological studies have established that analysis of the harmonics, amplitude and variability of ICPW provide a more detailed information of what is occurring dynamically inside the skull.58,59 Thus, the analysis of ICPW is a useful tool to gauge intracranial compliance. In summary, if P2 is sustained greater than or equal to P1, even with normal ICP values, indicates that compliance is reduced.58,59

Recently, a noninvasive system (Brain4care Corp, Sao Paulo, Brazil) was developed to detect and monitor the cranial cavity micro-expansions. Using a surface sensor, it allows obtaining an ICPW and the analysis of derived variables such as the relationship between the P1/P2 components and the time to reach the maximum peak (amplitude), which have shown a very good correlation with invasive ICP values.60,61 At the same time, the analysis of the ICPW morphology allows the evaluation of cerebral compliance, making it an integrative tool for the evaluation of the intracranial compartmental syndrome10,62 (Fig. 3).

Figure 3.

Stages in the development of the intracranial compartmental syndrome.

This device was validated in different populations of ABI patients, including stroke, TBI and hydrocephalus.60–62 A recent study using advanced machine learning models explored multiple waveform parameters to optimize mean ICP estimation. Approximately 72% of estimates from the validation sample were within 0−4 mmHg of invasive ICP values.63 The Bland–Altman analysis revealed a mean difference (bias) between ICP and estimated ICP of −0.21 mmHg and an SD of ±3.68 mmHg. There was a moderate relationship between ICP and estimated ICP (r = 0.43, P < .05).63 Nonetheless, the system has limitations. Patient agitation and manipulation can interfere with the sensor’s ability to acquire reliable waveforms. Additional constraints include the requirement for an internet connection for waveform processing.64