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HOME > Acute Crit Care > Volume 41(1); 2026 > Article
Review Article
Neurosurgery Personalized treatment approaches in neurocritical care
Acute and Critical Care 2026;41(1):33-46.
DOI: https://doi.org/10.4266/acc.003050
Published online: September 26, 2025

1Department of Neurosurgery, Keimyung University School of Medicine, Daegu, Korea

2Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

Corresponding author: Seungjoo Lee Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea Tel: +82-2-3010-3550, Fax: +82-2-476-6738, Email: changhill@gmail.com
• Received: July 25, 2025   • Accepted: September 1, 2025

© 2026 The Korean Society of Critical Care Medicine

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Acute brain injuries—including traumatic brain injury, subarachnoid hemorrhage, and intracerebral hemorrhage—exhibit profound pathophysiological heterogeneity, yet are often managed using standardized treatment protocols. While evidence-based guidelines have improved outcomes at a population level, they frequently overlook patient-specific variations in cerebral compliance, autoregulation, and metabolic reserve. This review explores the evolving paradigm of personalized neurocritical care, which integrates dynamic multimodal monitoring, individualized intracranial pressure management strategies, and real-time physiological indices such as pressure reactivity index, cerebral perfusion pressure optimization, and waveform analytics. We highlight the role of noninvasive modalities including quantitative pupillometry, transcranial Doppler, optic nerve sheath diameter ultrasound, near-infrared spectroscopy, and electroencephalography as adjuncts when invasive monitoring is limited or contraindicated. Furthermore, we examine tissue-level monitoring using brain oxygen tension and cerebral microdialysis and emerging blood-based biomarkers such as glial fibrillary acidic protein and neurofilament light. These tools provide granular insight into evolving secondary injury processes. In parallel, advances in artificial intelligence (AI) and machine learning enable deep phenotyping, predictive modeling, and integration of high-dimensional data including imaging, physiology, and omics-based profiles. The development of digital twin models further supports individualized simulation and therapeutic planning. While challenges remain in implementation, data harmonization, and resource availability, the convergence of physiologic monitoring, molecular profiling, and computational modeling offers a transformative pathway toward precision medicine in neurocritical care.
  • Key Words: brain injuries; critical care; intracranial pressure; precision medicine
Acute brain injuries such as traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), and intracerebral hemorrhage (ICH) represent a diverse spectrum of pathophysiological processes. Despite this heterogeneity, clinical management has traditionally relied on standardized, population-based treatment protocols. While evidence-based guidelines have improved outcomes at a population level, they often fall short in addressing the unique physiological and biological characteristics of individual patients. As a result, interventions such as blood pressure control, hematoma evacuation, or neuroprotective therapies may yield variable outcomes—not due to inefficacy per se, but to the inadequacy of uniform thresholds applied to heterogeneous patient populations (Figure 1).
Precision medicine, or personalized medicine, refers to the customization of healthcare based on unique individual profiles, encompassing not only genetic and epigenetic data but also lifestyle, environmental exposures, and real-time physiological parameters [1,2]. In neurocritical care, the adoption of personalized approaches is particularly imperative given the rapid and dynamic evolution of brain injury and the critical importance of timely intervention. The concept of personalized neurocritical care extends beyond the traditional scope of genomics and molecular profiling. It incorporates multimodal neuromonitoring, advanced neuroimaging, bedside biomarkers, and physiological data integration to guide clinical decision-making in real time [3]. Recent advances in artificial intelligence (AI) and machine learning further enhance our ability to synthesize complex datasets and identify individualized therapeutic targets (Figure 2).
The aim of this review is to outline the current landscape and future potential of personalized treatment strategies in neurocritical care. By shifting from population-based protocols to physiology-driven, patient-specific management, we seek to improve outcomes through precise, adaptive, and integrative care.
Intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care, particularly in patients with acute brain injuries such as TBI, SAH, or ICH. While current guidelines recommend treating sustained high ICP greater than 22 mm Hg and maintaining cerebral perfusion pressure (CPP) between 60–70 mm Hg, this standardized approach does not account for the substantial variability in individual patient physiology [4,5].
The brain’s ability to compensate for changes in volume is limited, and elevated ICP can lead to secondary brain injury if not promptly addressed. ICP monitoring provides crucial insight into intracranial dynamics and guides timely intervention. Common indications include severe TBI (Glasgow coma scale [GCS] ≤8), abnormal pupil reactivity, radiographic evidence of increased ICP, and unexplained neurological deterioration in patients with limited capability to undergo clinical assessment [4,5]. However, international studies have revealed substantial variability in ICP monitoring practices and outcomes, reflecting a need for more individualized approaches [6-10]. Factors such as age-related brain atrophy, differences in cerebral compliance, impaired autoregulation, and underlying pathology can all affect patient tolerance to high ICP [11]. Applying a uniform threshold to all patients may lead to under- or overtreatment. To address these limitations, personalized strategies for ICP management have been proposed. One such approach involves the use of the pressure reactivity index (PRx), which reflects the dynamic relationship between ICP and mean arterial pressure (MAP) [12,13]. A positive PRx indicates impaired cerebral autoregulation and suggests that the patient may not tolerate high CPP, whereas a negative PRx suggests intact autoregulation, in which higher CPP may be beneficial [13]. Based on this principle, the concept of optimal CPP has been introduced, allowing clinicians to deliver individualized perfusion targets that maximize autoregulatory efficiency [12]. Using platforms such as ICM+, an optimal CPP can be derived for each patient through continuous autoregulation monitoring [14]. This methodology demonstrates the clinical utility of PRx-guided management in tailoring therapy to a patient’s physiological status in real time.
Beyond static values, ICP waveform analysis offers additional insight into intracranial compliance (Figure 3). The waveform consists of three primary components [15]: Percussion wave (P1): reflects arterial pulsations transmitted via the choroid plexus; Tidal wave (P2): corresponds to pressure reflected from the brain parenchyma and is indicative of compliance; and Dicrotic wave (P3): represents closure of the aortic valve. As compliance deteriorates, the P2 peak becomes more prominent than P1, and the overall waveform amplitude increases. Parameters such as the P2/P1 ratio and mean wave amplitude can serve as early indicators of reduced compliance and evolving intracranial hypertension [16]. Additionally, the RAP index—the correlation coefficient (R) between AMP amplitude (A) and mean pressure (P)—provides insight into the compensatory reserve of the intracranial compartment [17,18]. Another important metric, the pressure time dose (PTD), quantifies the cumulative burden of ICP increases over time. Studies have demonstrated that PTD correlates more strongly with mortality than does mean ICP alone, underscoring the importance of evaluating temporal patterns rather than isolated values [19,20].
Ultimately, the goal of ICP management is to optimize CPP to ensure adequate cerebral blood flow without causing hyperemia or hypoxia. By integrating dynamic indices such as PRx and waveform morphology and burden metrics like PTD, clinicians can move beyond fixed thresholds and tailor interventions to unique patient physiology. Although personalized ICP monitoring holds great promise, its implementation requires advanced monitoring tools, technical expertise, and clinical validation. As evidence continues to accumulate, incorporating individualized targets into routine practice may represent the next frontier in neurocritical care.
ICP monitoring remains the clinical standard for managing patients with severe acute brain injury. However, it has important limitations. In low- and middle-income countries, these devices may be unavailable due to cost or infrastructure constraints. In patients with coagulopathies—such as those with acute liver failure or thrombocytopenia—placement is contraindicated due to bleeding risk. Even in high-resource settings, invasive monitoring carries inherent risks, including hemorrhage, infection, and procedural complications [21]. It also requires neurosurgical expertise and continuous maintenance, limiting its widespread use.
These limitations have driven growing interest in noninvasive strategies for assessing and monitoring intracranial dynamics [22]. Clinically, elevated ICP may be suspected when a patient shows a sudden decrease in GCS score, loss of pupillary light reflex, or computed tomography (CT) findings such as effacement of the basal cisterns. However, these signs are intermittent, often subjective, and may be unreliable in sedated or ventilated patients. Neuroimaging provides only snapshots in time and lacks the capacity for continuous trend monitoring, which is crucial in the neurocritical care setting. To address these gaps, several noninvasive tools have been developed and integrated into multimodal monitoring strategies. These include automated pupillometry, optic nerve sheath diameter (ONSD) ultrasound, transcranial Doppler (TCD), near-infrared spectroscopy (NIRS), and electroencephalography (EEG). While none of these methods can currently replace invasive ICP monitoring, they serve as valuable adjuncts, particularly in patients unsuitable for invasive approaches (Table 1).
Quantitative Pupillometry
Traditionally, pupillary assessments were performed manually using penlights, leading to high interobserver variability. In contrast, modern pupillometers offer objective, real-time, and reproducible measurements. They provide a suite of parameters including pupil size, constriction velocity, latency, dilation speed, and the Neurological Pupil Index (NPi)—a proprietary algorithm-derived score ranging from 0 to 5. NPi values less than 3 are generally considered abnormal [23]. Quantitative pupillometry is noninvasive, portable, and can be easily repeated at the bedside. It is especially useful in patients with limited or unreliable neurological exams, such as those under sedation or paralysis. It also supports early detection of neurological decline and facilitates standardized communication between providers. However, it is important to understand the physiological basis and limitations of NPi. The pupillary light reflex involves a complex neural circuit, and disruptions anywhere along the pathway—from the retina to the midbrain—can affect reactivity. In cases of transtentorial herniation, for example, NPi values often decrease in parallel with ICP elevation [24]. Conversely, lesions involving the optic nerve or midbrain may result in a low NPi without increased ICP. As such, while NPi offers valuable insight, it does not precisely represent direct pressure measurement. In summary, noninvasive ICP monitoring techniques are expanding the horizon of neurocritical care, offering complementary data to guide clinical decisions when invasive monitoring is unavailable or contraindicated. While current methods have limitations, their integration into multimodal strategies may enhance patient safety, allow earlier detection of deterioration, and improve global access to ICP assessment.
Optic Nerve Sheath Diameter
The optic nerve sheath is anatomically continuous with the subarachnoid space and contains cerebrospinal fluid (CSF). When intracranial pressure increases, this elevated CSF pressure is transmitted along the sheath, leading to measurable expansion. This physiological relationship forms the basis for using ONSD as a noninvasive surrogate marker for increased ICP [25]. ONSD can be quickly and safely measured at the bedside using ultrasound [26]. In general, an ONSD greater than 5.0 mm suggests elevated ICP, though reported cutoff values range from 5.0 to 5.9 mm [27]. Normal ONSD values are typically between 3.5 and 4.8 mm [28-31]. To obtain accurate measurements, a linear ultrasound probe is gently placed on the closed eyelid of a supine patient. The sheath is measured 3 mm posterior to the globe, from inner edge to inner edge of the hyperechoic sheath [32]. While ONSD has demonstrated good intra- and interobserver reliability in individual studies, its clinical standardization is limited by methodological variability. Factors such as ultrasound instrument settings, probe angle, and sheath boundary definition contribute to inconsistent measurements [32]. Therefore, ONSD is best used as a trend-based tool rather than a single-value cutoff. With appropriate training and protocol standardization, ONSD has potential as a reliable adjunct in situations where invasive monitoring is not feasible.
Transcranial Doppler
TCD is a noninvasive ultrasound technique that measures blood flow velocity in major intracranial arteries, such as the middle cerebral artery. TCD uses low-frequency probes (≤2 MHz) through cranial acoustic windows—most commonly the temporal window—to assess flow direction and velocity based on Doppler principles [33,34]. In the intensive care unit (ICU), TCD is most widely used to monitor vasospasm following aneurysmal SAH, guide endovascular therapy, and assess treatment responses to vasodilators. Additional indications include cerebral hyperperfusion after carotid interventions, detection of re-occlusion or embolic signals in ischemic stroke, and individualized blood pressure management [35,36]. TCD offers high temporal resolution and is portable and repeatable, ideal for serial bedside monitoring. However, up to 10%–15% of patients have poor acoustic windows, and the accuracy of insonation depends heavily on operator skill [37]. Continuous monitoring is also limited by the need for sustained manual positioning, though robotic TCD systems are under development to address this issue [38]. When incorporated into a multimodal framework, TCD provides valuable real-time hemodynamic data for precision care.
Near-Infrared Spectroscopy
NIRS is a noninvasive method for monitoring regional cerebral oxygen saturation (rSO₂). It works by emitting near-infrared light (650–950 nm) through the scalp and skull, where it interacts with oxyhemoglobin and deoxyhemoglobin in cerebral tissue [39]. The reflected light is analyzed to estimate cerebral oxygenation in real time. NIRS is particularly valuable in deeply sedated, mechanically ventilated, or hemodynamically unstable patients who cannot undergo frequent clinical assessments or invasive monitoring [40]. Modern NIRS devices attempt to subtract extracranial signal using dual-detector systems, though signal contamination remains a challenge, especially for deeper structures [41]. There are three main types of NIRS devices: continuous-wave, which tracks saturation trends; frequency-domain, which allows estimation of absolute values; and time-resolved spectroscopy, which enhances depth resolution. Regardless of type, NIRS values should be interpreted contextually. A decrease in rSO₂ greater than 20% from baseline is typically a red flag for ischemia and should receive prompt intervention. Importantly, NIRS should be interpreted alongside other parameters such as hemoglobin, PaO₂, MAP, and ICP. NIRS is not a substitute for invasive monitoring but serves as a valuable early warning system. It can detect cerebral desaturation before clinical signs or structural changes appear and may guide real-time decisions in acute stroke and perioperative care [42]. When integrated with multimodal tools, NIRS enhances our ability to maintain adequate cerebral oxygenation and prevent secondary injury.
Electroencephalography
EEG is increasingly recognized as a functional, noninvasive monitor of cerebral physiology, extending beyond its traditional role in seizure detection. Continuous EEG (cEEG) allows real-time assessment of both cortical and subcortical activity and can detect changes in cerebral blood flow, pressure, and metabolism [43]. In the ICU, cEEG is routinely used for detecting nonconvulsive seizures or status epilepticus—conditions that affect up to 20%–30% of critically ill patients, often without clinical signs [44,45]. Guidelines from the American Heart Association recommend EEG monitoring in patients with persistent altered mental status following cardiac arrest [46,47]. EEG abnormalities can precede irreversible brain injury by minutes to hours, offering a crucial window for intervention [48]. Beyond seizure detection, EEG can identify ischemia-related changes such as loss of fast frequencies, progressive slowing, and burst suppression. Quantitative EEG parameters, such as alpha-delta ratio, suppression index, and seizure panels, further enhance its utility for sedation titration and prognostication. Despite its strengths, cEEG has logistical challenges: it requires specialized equipment, trained technicians, and expert interpretation. Sedation and neuromuscular blockade can also affect signal quality. Emerging technologies, such as simplified or wearable EEG devices with automated interpretation, may help expand access and reduce resource burdens [49]. When used in conjunction with other noninvasive tools, EEG provides functional insight into cerebral dynamics and supports early, targeted intervention—especially in patients unsuitable for clinical exams.
ICP monitoring has long been considered the cornerstone of neurocritical care, particularly in patients with TBI. However, while ICP provides essential information about global cerebral pressure dynamics, it offers only a limited window into the underlying pathophysiological processes. It does not reflect local tissue oxygenation, metabolic status, or biochemical markers of cellular distress. As secondary brain injury often evolves at a molecular and regional level before manifesting as elevated ICP, there is a growing need to incorporate personalized biomarkers into clinical decision-making. To address this gap, multimodal monitoring tools have been introduced to allow clinicians to assess brain oxygenation and metabolism in real time. Among the most promising of these are brain tissue oxygen pressure (PbtO₂) monitoring and cerebral microdialysis. These modalities offer greater insight into cerebral physiology, enabling clinicians to identify early signs of cellular stress and tailor interventions accordingly.
PbtO₂ monitoring involves placing a probe directly into brain tissue to measure local oxygen tension. It provides continuous, focal data that can reveal regional hypoxia even when ICP and CPP remain within target ranges. The commonly used threshold is PbtO₂ greater than 20 mm Hg [50]. If levels fall below this target, interventions such as increasing MAP, optimizing ventilator settings, or adjusting inspired oxygen can be initiated to improve oxygen delivery [51,52]. Studies have shown that incorporating PbtO₂ into goal-directed therapy can improve outcomes, although results from large trials have been mixed [53]. For instance, the OXY-TC trial did not demonstrate a clear benefit from dual ICP and PbtO₂ monitoring, though post hoc analysis suggested improved functional outcomes in patients with initially elevated ICP [54]. Further trials like BOOST-III and BONANZA are currently underway to better define the patient populations most likely to benefit [55].
Cerebral microdialysis, another powerful tool, involves the insertion of a fine catheter into vulnerable brain regions to continuously sample extracellular fluid [50]. Through this technique, clinicians can monitor key metabolic markers such as glucose, lactate, pyruvate, glutamate, and glycerol. Each of these reflects a different aspect of cerebral metabolism [56]. For example, low glucose levels may signal neuroglycopenia, while an elevated lactate-to-pyruvate ratio (LPR) suggests ischemia or mitochondrial dysfunction. Elevated glutamate concentrations are indicative of excitotoxicity, and rising glycerol levels point to cell membrane breakdown and oxidative stress. When interpreted together, these parameters provide a real-time picture of ongoing injury at the cellular level, even before structural changes are apparent on imaging or clinical signs emerge.
Despite their promise, both PbtO₂ and microdialysis face barriers to widespread clinical adoption. These include the need for specialized equipment and expertise, delayed availability of microdialysis results, and the highly localized nature of data, which may not reflect global brain status [57,58]. Additionally, the cost and technical complexity of these systems currently restrict their use to specialized academic centers or high-resource neurocritical care units.
Beyond catheter-based monitoring, interest is also growing in blood-based biomarkers that may offer a systemic view of cerebral injury. Markers such as neurofilament light chain (NFL), tau protein, and glial fibrillary acidic protein (GFAP) are being explored for their roles in diagnosing and prognosticating TBI, stroke, and other acute neurological conditions [59-61]. NFL and tau reflect axonal and cytoskeletal damage, respectively, while GFAP is associated with astrocyte activation and blood-brain barrier disruption. These markers can be measured from blood samples, offering a less invasive alternative to intracranial probes and enabling serial monitoring over time. While their clinical integration is in early stages, such biomarkers may complement existing tools and help guide individualized therapy.
Ultimately, the value of personalized biomarkers lies in their integration. Modern software platforms allow real-time visualization of ICP, PbtO₂, microdialysis data, and systemic physiological parameters. This multimodal approach enables clinicians to detect subtle physiological shifts and dynamically adjust treatment. For example, simultaneous increases in ICP may prompt escalation to higher-tier therapies, while isolated decreases in PbtO₂ could indicate the need for respiratory or hemodynamic optimization [62]. Similarly, elevated LPR or low glucose levels identified via microdialysis may signal an impending metabolic crisis, prompting interventions such as increasing systemic glucose delivery or enhancing substrate availability [62]. In this way, therapy becomes responsive not to static guidelines, but to the patient’s own evolving biology.
In summary, while ICP remains essential in the acute management of brain injury, it does not tell the full story. Personalized biomarkers—whether derived from tissue probes or circulating in the blood—offer a deeper understanding of brain pathophysiology and open the door to truly individualized neurocritical care. As these technologies continue to mature, their thoughtful integration into practice will be key to improving outcomes in patients with complex cerebral injuries.
Neurocritical care involves the management of some of the most complex and dynamically unstable patients in medicine. These patients generate massive volumes of data across multiple domains—including invasive and noninvasive monitoring (such as ICP, PbtO₂, EEG, NIRS, TCD, and other physiologic or imaging modalities), serial neuroimaging (including CT, magnetic resonance imaging, and perfusion studies), continuous laboratory results, and time-stamped records of interventions, medications, and vital signs documented in the electronic health record [63]. Although this wealth of information holds substantial clinical value, it is often underutilized due to the limitations of traditional data interpretation methods.
Historically, neurocritical care has relied on snapshot-based clinical assessments and linear thinking—approaches that fail to capture the dynamic, multidimensional nature of acute brain injury. Static guidelines cannot adapt to the evolving physiology of the injured brain, and human cognition alone struggles to manage the complexity of real-time ICU data. This is where AI and machine learning offer a transformative opportunity. Early applications of machine learning in neurocritical care focused primarily on outcome prediction, such as forecasting 3-month modified Rankin Scale scores or mortality [64-67]. While useful, these models often suffered limited interpretability, risked self-fulfilling prophecies in end-of-life decision-making, and were prone to overfitting due to narrow training datasets with limited generalizability.
A more promising direction lies in deep phenotyping—a data-intensive approach that seeks not only to predict outcomes, but to understand and define patient-specific traits. Deep phenotyping integrates multimodal data including clinical variables (e.g., age, comorbidities, neurologic exam), imaging features (e.g., lesion location and volume), physiologic trends (e.g., ICP, PbtO₂, LPR), and emerging molecular data (e.g., genomics and proteomics). By clustering patients into biologically meaningful subgroups, this approach enables more precise treatment selection and enhances the design and success of clinical trials. The need for such stratification is illustrated in recent clinical trial history. For example, the surgical trial in intracerebral haemorrhage and minimally invasive surgery plus rt-PA for intracerebral hemorrhage evacuation trials failed to demonstrate significant benefit from hematoma evacuation in spontaneous ICH—largely due to nonselective inclusion criteria and delayed intervention [68,69]. In contrast, the ENRICH trial applied early, minimally invasive surgery in patients with favorable imaging phenotypes (lobar or anterior ganglia location) and demonstrated significantly improved functional outcomes [70]. Similarly, in ischemic stroke, early trials like interventional management of stroke III and mechanical retrieval and recanalization of stroke clots using embolectomy failed due to lack of perfusion-based selection [71,72]. Later trials such as diffusion-weighted imaging or computerized tomography perfusion assessment with clinical mismatch in the triage of wake-up and late presenting strokes undergoing neurointervention and endovascular therapy following imaging evaluation for ischemic stroked III used imaging to define salvageable penumbra, excluded patients with large infarcts, and showed a dramatic benefit of thrombectomy even in extended time windows [73,74]. These examples confirm that phenotype-driven selection—not just intervention timing—is key to optimizing outcomes.
AI also plays an expanding role in predictive analytics. A recent study treated elevated ICP as a dynamic phenotype and used a recurrent neural network to forecast sustained ICP crises (≥22 mm Hg) more than 2 hours in advance [75]. The model, trained on 1,346 patients and validated on public datasets like MIMIC and electronic ICU, handled sparse, time-series ICU data robustly and identified key predictors such as ICP, CPP, MAP, and laboratory values. Feature attribution techniques improved interpretability, and predictive performance increased as the time window to the event narrowed. This work demonstrates that reliable, clinically actionable early warnings are achievable, potentially enabling proactive intervention before irreversible deterioration occurs.
Despite this progress, implementation challenges remain. Clinical data are frequently fragmented across disparate electronic health record systems, inconsistently labeled, and recorded in unstructured formats. To operationalize AI and deep phenotyping at scale, a unified data infrastructure is essential—incorporating structured data capture (e.g., National Institutes of Health/National Institute of Neurological Disorders and Stroke Common Data Elements), natural language processing to extract insights from clinical narratives, and AI-integrated dashboards that synthesize real-time physiological, imaging, and molecular data into intuitive decision support tools. This convergence marks a paradigm shift from reactive, guideline-based care to proactive, precision-driven management. By identifying patients most likely to benefit from specific interventions—and when—clinicians can deliver targeted therapy, reduce unnecessary interventions, and improve patient outcomes. As data science, technology, and clinical neurocritical care converge, AI has the potential to become an indispensable partner in the management of critically ill neurologic patients.
Acute brain injury (ABI) is a progressive, dynamic process rather than a singular event. Traditional classifications—such as GCS scores or focal vs. diffuse distinctions—fail to capture the complexity and evolution of secondary brain injury. To advance beyond these limitations, emerging technologies are shaping a pathophysiology-based framework that integrates advanced monitoring, high-dimensional biological profiling, and real-time computational modeling.
Among such methods, multi-omics technologies offer unprecedented insights into the molecular landscape of ABI [3]. Genomics, transcriptomics, proteomics, metabolomics, and epigenomics each provide unique perspectives on disease mechanisms [76]. Collectively, they enable a systems-level view of injury progression and repair. In TBI and acute ischemic stroke, omics approaches are increasingly employed to discover biomarkers, stratify patients by molecular subtypes, and identify novel therapeutic targets [77,78]. For example, metabolomics can bridge genotype and phenotype via downstream biochemical profiles detectable in blood, CSF, or urine. As treatment responses vary greatly between individuals, understanding and addressing biological heterogeneity has become crucial. Omics-guided patient stratification enables a more nuanced understanding of disease mechanisms and supports reverse translational research, where patient-derived molecular profiles inform the development of targeted interventions [79]. Machine learning algorithms are increasingly used to integrate multi-omics datasets, revealing upstream pathological triggers and downstream molecular cascades.
However, omics alone is insufficient for clinical translation. Digital twin models—virtual, patient-specific replicas of brain physiology created by integrating imaging, electrophysiology, and molecular data—offer a bridge to bedside application [80,81]. These in silico models enable clinicians to simulate and optimize interventions, such as surgery or neurostimulation, before applying them in vivo. Applications in epilepsy, stroke, TBI, and neurodegenerative diseases are rapidly expanding [82]. The rise of generalist medical AI systems also holds promise [80]. These models integrate multimodal data—clinical records, imaging, lab values, and omics—to support real-time diagnostics, risk stratification, and individualized decision-making. In ABI, they may enhance early detection of secondary injury and guide personalized care pathways.
Nonetheless, challenges persist. Data remain siloed, non-standardized, and difficult to harmonize. High-dimensional omics datasets demand advanced algorithms to manage noise, missing data, and heterogeneity. Large-scale, multicenter collaboration is essential to validate predictive models and build interoperable infrastructure. Initiatives like the NIH/NINDS Common Data Elements, natural language processing, and integrative visualization platforms are helping standardize terminology and improve clinical usability. Ultimately, the future of neurocritical care lies in the convergence of omics, AI, and digital modeling. Together, they enable a shift from diagnosis- or protocol-driven care to precision medicine tailored to evolving patient pathophysiology. Realizing this vision will require not only technological innovation but also organizational transformation—driven by multidisciplinary collaboration across clinicians, neuroscientists, data scientists, and engineers. With integration, automation, and personalization, we may finally overcome longstanding barriers and usher in an era of intelligent, individualized neurocritical care.
ABI is a dynamic and heterogeneous condition that challenges conventional, protocol-based management. Standardized thresholds often fail to reflect individual variability in cerebral compliance, autoregulation, and metabolic reserve. Advances in multimodal monitoring—such as PRx-guided CPP targets, ICP waveform analysis, and brain tissue oxygen or metabolic biomarkers—are enabling more personalized, physiology-driven interventions. These tools offer real-time insight into secondary injury and support earlier, more tailored responses. AI and deep phenotyping further enhance this approach by integrating complex clinical, imaging, and molecular data, allowing predictive modeling and patient-specific risk stratification. Emerging technologies, including multi-omics and digital twin simulations, promise to transform neurocritical care into a truly precision-based discipline. While barriers to implementation remain, the path forward is clear: personalization, integration, and intelligent decision support will define the next era of neurocritical care.
• Personalized neurocritical care integrates patient-specific physiological, imaging, and molecular data to optimize treatment strategies beyond traditional protocols.
• Advanced tools such as pressure reactivity index-guided cerebral perfusion pressure targets, noninvasive intracranial pressure surrogates, and brain-specific biomarkers allow real-time, individualized monitoring and intervention.
• Artificial intelligence (AI)-driven analytics and multi-omics technologies enable deep phenotyping and predictive modeling, paving the way for precision medicine in managing acute brain injury.

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

FUNDING

None.

ACKNOWLEDGMENTS

None.

AUTHOR CONTRIBUTIONS

Conceptualization: JHK, SL. Data curation: CHK. Visualization: HJ. Writing - original draft: JHK, SL. Writing - review & editing: JHK, CHK, SL. All authors read and agreed to the published version of the manuscript.

Figure 1.
Transition to personalized neurocritical care. The figure highlights a shift from standardized protocols to personalized treatment in managing acute brain injuries. Modern approaches incorporate multimodal monitoring, molecular biomarkers, artificial intelligence (AI)-driven analytics, and precision medicine to tailor care based on individual patient characteristics.
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Figure 2.
Evolving components of precision medicine in acute brain injury. This figure illustrates the expanding scope of precision medicine in neurocritical care. While traditional approaches have focused on genomics, biomarkers, metabolomics, and phenotyping, recent advancements incorporate digital health, wearable devices, artificial intelligence, integrative omics, and adaptive clinical trial designs. These innovations enhance real-time, individualized decision-making for patients with acute brain injury.
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Figure 3.
Intracranial pressure (ICP) waveform morphology and compliance assessment. This figure illustrates normal and pathological ICP waveform components and their clinical significance. The ICP waveform consists of three peaks: P1 (percussion wave, arterial pulsation), P2 (tidal wave, brain compliance), and P3 (dicrotic wave, aortic valve closure). In normal conditions, P1>P2. As intracranial compliance deteriorates, P2 becomes equal to or exceeds P1 (P2/P1≥1), indicating reduced compensatory reserve. The lower right panel demonstrates waveform metrics of rise time, amplitude, and slope (dP/dT), which, along with the P2/P1 ratio and RAP index, serve as early indicators of evolving intracranial hypertension.
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Table 1.
Comparison of noninvasive neuromonitoring modalities
Modality Principle Advantage Limitation Clinical application
Quantitative pupillometry Measures pupil light reflex parameters (size, velocity, latency, NPi) using an automated device Noninvasive and portable May be falsely low in optic nerve/midbrain lesions Monitoring in patients with unreliable exams
Provides objective, standardized assessments Cannot fully replace direct ICP monitoring Early detection of neurologic decline
Useful in sedated/paralyzed patients Indirect ICP estimation
Optic nerve sheath diameter Ultrasound-based measurement of optic nerve sheath dilation in response to raised ICP Quick and safe bedside tool Inter-operator variability ICP screening and follow-up
Noninvasive and repeatable Lack of universal standardization Alternative when invasive monitoring is contraindicated
Good sensitivity for elevated ICP Affected by anatomical variability
Transcranial Doppler Doppler ultrasound to assess blood flow velocity in intracranial arteries Real-time hemodynamic monitoring Poor acoustic windows in 10%–15% of patients Vasospasm monitoring after a SAH
Bedside and repeatable Operator-dependent Blood pressure titration
Applicable to various neurovascular conditions Limited continuous monitoring capability Real-time cerebral blood flow assessment
Near-infrared spectroscopy Measures regional cerebral oxygen saturation (rSO₂) using near-infrared light Continuous oxygenation monitoring Signal contamination from extracranial tissue Early ischemia detection
Useful in deeply sedated or unstable patients Limited depth resolution Intraoperative and acute stroke care
Early warning for hypoxia Values must be interpreted contextually Part of multimodal neuromonitoring
Electroencephalography Measures cortical electrical activity; continuous electroencephalography enables real-time monitoring Functional brain monitoring Requires trained personnel and interpretation Seizure/NCSE detection
Detects non-convulsive seizures Affected by sedation/paralytics Prognostication after brain injury or cardiac arrest
Prognostic value in comatose patients Resource-intensive Monitoring cerebral ischemia

NPi: Neurological Pupil Index; ICP: intracranial pressure; SAH: subarachnoid hemorrhage; NCSE: nonconvulsive status epilepticus.

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        Personalized treatment approaches in neurocritical care
        Acute Crit Care. 2026;41(1):33-46.   Published online December 8, 2025
        DOI: https://doi.org/10.4266/acc.003050
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      Personalized treatment approaches in neurocritical care
      Image Image Image Image
      Figure 1. Transition to personalized neurocritical care. The figure highlights a shift from standardized protocols to personalized treatment in managing acute brain injuries. Modern approaches incorporate multimodal monitoring, molecular biomarkers, artificial intelligence (AI)-driven analytics, and precision medicine to tailor care based on individual patient characteristics.
      Figure 2. Evolving components of precision medicine in acute brain injury. This figure illustrates the expanding scope of precision medicine in neurocritical care. While traditional approaches have focused on genomics, biomarkers, metabolomics, and phenotyping, recent advancements incorporate digital health, wearable devices, artificial intelligence, integrative omics, and adaptive clinical trial designs. These innovations enhance real-time, individualized decision-making for patients with acute brain injury.
      Figure 3. Intracranial pressure (ICP) waveform morphology and compliance assessment. This figure illustrates normal and pathological ICP waveform components and their clinical significance. The ICP waveform consists of three peaks: P1 (percussion wave, arterial pulsation), P2 (tidal wave, brain compliance), and P3 (dicrotic wave, aortic valve closure). In normal conditions, P1>P2. As intracranial compliance deteriorates, P2 becomes equal to or exceeds P1 (P2/P1≥1), indicating reduced compensatory reserve. The lower right panel demonstrates waveform metrics of rise time, amplitude, and slope (dP/dT), which, along with the P2/P1 ratio and RAP index, serve as early indicators of evolving intracranial hypertension.
      Graphical abstract

      Figure 1.

      Figure 2.

      Figure 3.

      Graphical abstract

      Personalized treatment approaches in neurocritical care
      Modality Principle Advantage Limitation Clinical application
      Quantitative pupillometry Measures pupil light reflex parameters (size, velocity, latency, NPi) using an automated device Noninvasive and portable May be falsely low in optic nerve/midbrain lesions Monitoring in patients with unreliable exams
      Provides objective, standardized assessments Cannot fully replace direct ICP monitoring Early detection of neurologic decline
      Useful in sedated/paralyzed patients Indirect ICP estimation
      Optic nerve sheath diameter Ultrasound-based measurement of optic nerve sheath dilation in response to raised ICP Quick and safe bedside tool Inter-operator variability ICP screening and follow-up
      Noninvasive and repeatable Lack of universal standardization Alternative when invasive monitoring is contraindicated
      Good sensitivity for elevated ICP Affected by anatomical variability
      Transcranial Doppler Doppler ultrasound to assess blood flow velocity in intracranial arteries Real-time hemodynamic monitoring Poor acoustic windows in 10%–15% of patients Vasospasm monitoring after a SAH
      Bedside and repeatable Operator-dependent Blood pressure titration
      Applicable to various neurovascular conditions Limited continuous monitoring capability Real-time cerebral blood flow assessment
      Near-infrared spectroscopy Measures regional cerebral oxygen saturation (rSO₂) using near-infrared light Continuous oxygenation monitoring Signal contamination from extracranial tissue Early ischemia detection
      Useful in deeply sedated or unstable patients Limited depth resolution Intraoperative and acute stroke care
      Early warning for hypoxia Values must be interpreted contextually Part of multimodal neuromonitoring
      Electroencephalography Measures cortical electrical activity; continuous electroencephalography enables real-time monitoring Functional brain monitoring Requires trained personnel and interpretation Seizure/NCSE detection
      Detects non-convulsive seizures Affected by sedation/paralytics Prognostication after brain injury or cardiac arrest
      Prognostic value in comatose patients Resource-intensive Monitoring cerebral ischemia
      Table 1. Comparison of noninvasive neuromonitoring modalities

      NPi: Neurological Pupil Index; ICP: intracranial pressure; SAH: subarachnoid hemorrhage; NCSE: nonconvulsive status epilepticus.

      Table 1.

      ACC : Acute and Critical Care
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