Predicting the emergence of malignant brain oedema in acute ischaemic stroke: a prospective multicentre study with development and validation of predictive modelling

Neuroimaging predictors of malignant brain oedema after thrombectomy in ischemic stroke: a systematic review and meta-analysis

BACKGROUND

We systematically reviewed neuroimaging predictors for malignant brain oedema (MBE) after thrombectomy in patients with ischemic stroke.

METHODS

We searched MEDLINE and EMBASE in November 2023 for studies of patients with ischemic stroke. We included studies investigating neuroimaging predictors or prediction models for MBE after thrombectomy. We estimated effect size for the association between predictors and MBE by odds ratios (ORs) or standardized mean differences (SMDs), and pooled results using random-effects modelling.

RESULTS

We included 19 studies (n = 6007) with 17 neuroimaging factors and 5 models. Lower Alberta Stroke Program Early CT scores (ASPECTS, n = 3052, SMD -1.84, 95% CI -2.52 - -1.16; df = 9) and longer extent of arterial occlusion at baseline were associated with higher risk of MBE. Post-thrombectomy ASPECTS was associated with MBE in general stroke patients (n = 453, SMD -2.91, -4.02 - -1.79; df = 1), but not in successfully reperfused patients (n = 110, SMD 0.24, -0.16 - 0.65). Successful reperfusion reduced risk of MBE (n = 4851, OR 0.39, 0.30-0.51; df = 13). Contrast enhancement on CT after thrombectomy was associated with higher risk of MBE (n = 998, OR 4.82, 2.53-9.20; df = 4). More reserved brain volume capacity (baseline: n = 683, OR 0.83, 0.77-0.91, p < .001; post-thrombectomy: n = 329, OR 0.53, 0.37-0.77, p < .001) and good collaterals (baseline: n = 2301, OR 0.14, 0.10-0.20, df = 3; post-thrombectomy: n = 1006, OR 0.28, 0.15-0.51; df = 2) were associated with lower risk of MBE.

CONCLUSION

Lower ASPECTS and longer arterial occlusion at baseline, and post-thrombectomy CT contrast enhancement increased risk of MBE. Reperfusion after thrombectomy, more reserved brain volume and good collaterals at baseline and post-thrombectomy reduced its risk.

Hybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery stroke

In treating malignant cerebral edema after a large middle cerebral artery stroke, clinicians need quantitative tools for real-time risk assessment. Existing predictive models typically estimate risk at one, early time point, failing to account for dynamic variables. To address this, we developed Hybrid Ensemble Learning Models for Edema Trajectory (HELMET) to predict midline shift severity, an established indicator of malignant edema, over 8-h and 24-h windows. The HELMET models were trained on retrospective data from 623 patients and validated on 63 patients from a different hospital system, achieving mean areas under the receiver operating characteristic curve of 96.6% and 92.5%, respectively. By integrating transformer-based large language models with supervised ensemble learning, HELMET demonstrates the value of combining clinician expertise with multimodal health records in assessing patient risk. Our approach provides a framework for accurate, real-time estimation of dynamic clinical targets using human-curated and algorithm-derived inputs.

Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning

This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritumoral edema in metastatic brain tumor patients by integrating advanced machine learning (ML) techniques with comprehensive imaging and clinical data. A retrospective analysis was performed on 300 patients who received BEV treatment from September 2013 to January 2024. The dataset incorporated 13 predictive features: 8 clinical variables and 5 radiological variables. The dataset was divided into a training set (70%) and a test set (30%) using stratified sampling. Data preprocessing was carried out through methods such as handling missing values with the MICE method, detecting and adjusting outliers, and feature scaling. Four algorithms, namely Random Forest (RF), Logistic Regression, Gradient Boosting Tree, and Naive Bayes, were selected to construct binary classification models. A tenfold cross-validation strategy was implemented during training, and techniques like regularization, hyperparameter optimization, and oversampling were used to mitigate overfitting. The RF model demonstrated superior performance, achieving an accuracy of 0.89, a precision of 0.94, F1-score of 0.92, with both AUC-ROC and AUC-PR values reaching 0.91. Feature importance analysis consistently identified edema volume as the most significant predictor, followed by edema index, patient age, and tumor volume. Traditional multivariate logistic regression corroborated these findings, confirming that edema volume and edema index were independent predictors (p < 0.01). Our results highlight the potential of ML-driven predictive models in optimizing BEV treatment selection, reducing unnecessary treatment risks, and improving clinical decision-making in neuro-oncology.

Effects of Intensive Blood Pressure Lowering on Brain Swelling in Thrombolyzed Acute Ischemic Stroke: The ENCHANTED Results

BACKGROUND

Cerebral swelling in relation to cytotoxic edema is a predictor of poor outcome in acute ischemic stroke (AIS) and elevated blood pressure (BP) promotes its development. Whether intensive BP-lowering treatment reduces cerebral swelling is uncertain. We aimed to determine whether intensive BP lowering reduces the severity of cerebral swelling after thrombolysis for AIS.

METHODS

A secondary analysis of the ENCHANTED (Enhanced Control of Hypertension and Thrombolysis Stroke Study), a partial factorial, international, multicenter, open-label, blinded end point, randomized controlled trial of alteplase dose and levels of BP control in thrombolyzed patients with AIS. Participants were randomly assigned to intensive (systolic target 130-140 mm Hg within 1 hour; maintained for 72 hours) or guideline-recommended (systolic target <180 mm Hg) BP management. Available serial brain images (baseline and follow-up, computed tomography, or magnetic resonance imaging) were centrally analyzed with standardized techniques (Apollo MIStar software) by expert readers blind to clinical details to rate swelling severity (from 0 no to 6 most severe swelling [midline shift and effacement of basal cisterns]) and other abnormalities. Primary outcome was any cerebral swelling (score, 1-6) in logistic regression models.

RESULTS

Of 1477/2196 (67.3%) patients (mean age, 67.7 years; female, 39.6%) with sequential scans, the between-group mean systolic BP difference was 6.6 mm Hg over 24 hours. No significant difference was found in the treatment effect on any cerebral swelling between intensive and guideline-recommended BP management (22.12% versus 22.39%, adjusted odds ratio, 1.05 [95% CI, 0.81-1.36]; P=0.71). Results were consistent across different groups of swelling severity (swelling score 2-6, 3-6, and 4-6; and ordinal shift on swelling score).

CONCLUSIONS

Modest early intensive BP lowering does not seem to alter cerebral swelling in thrombolyzed patients with AIS. Further research is needed to quantify brain edema to allow a better understanding of the complex relations of BP and outcomes from AIS.

Clinical course, causes of worsening, and outcomes of severe ischemic stroke: A prospective multicenter cohort study

BACKGROUND

Severe stroke has high rates of mortality and morbidity. This study aimed to investigate the clinical course, causes of worsening, and outcomes of severe ischemic stroke.

METHODS

This prospective, multicenter cohort study enrolled adult patients admitted ≤30 days after ischemic stroke from nine hospitals in China between September 2017 and December 2019. Severe stroke was defined as a score of ≥15 on the National Institutes of Health Stroke Scale (NIHSS). Clinical worsening was defined as an increase of 4 in the NIHSS score from baseline. Unfavorable functional outcome was defined as a modified Rankin scale score ≥3 at 3 months and 1 year. We per-formed logistic regression to explore baseline features and reperfusion therapies associated with clinical worsening and functional outcomes.

RESULTS

Among 4201 patients enrolled, 854 patients (20.33%) had severe stroke on admission. Of 3347 patients without severe stroke on admission, 142 (4.24%) patients developed severe stroke in hospital. Of 854 patients with severe stroke on admission, 33.95% (290/854) experienced clinical worsening (time from stroke onset median: 43 h, interquartile range [IQR]: 20-88 h), with brain edema (54.83% [159/290]) as the leading cause; 24.59% (210/854) of these patients died by 30 days, and 81.47% (677/831) and 78.44% (633/807) had unfavorable functional outcomes at 3 months and 1 year, respectively. Reperfusion reduced the risk of worsening (adjusted odds ratio [OR]: 0.24, 95% confidence interval [CI]: 0.12-0.49, P <0.01), 30-day death (adjusted OR: 0.22, 95% CI: 0.11-0.41, P <0.01), and unfavorable functional outcomes at 3 months (adjusted OR: 0.24, 95% CI: 0.08-0.68, P <0.01) and 1 year (adjusted OR: 0.17, 95% CI: 0.06-0.50, P <0.01).

CONCLUSIONS

Approximately one-fifth of patients with ischemic stroke had severe neurological deficits on admission. Clinical worsening mainly occurred in the first 3 days after stroke onset, with brain edema as the leading cause of worsening. Reperfusion reduced the risk of clinical worsening and improved functional outcomes.

REGISTRATION

ClinicalTrials.gov, NCT03222024.

Advances in the Critical Care of Ischemic Brain Infarction

Acute care for ischemic stroke has dramatically evolved over the last years. Cerebral reperfusion is possible up to 24 h after symptoms onset. Advanced brain imaging allows identifying salvageable ischemic brain tissue, and the development of newer endovascular devices permits access to distal vessels. Monitoring for neurologic deterioration, diagnosis of stroke etiology, and secondary prevention treatments are important after initial treatment. This article reviews the recent advancements in the critical care of acute ischemic stroke.

Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta-Analysis

INTRODUCTION

Stroke patients are at high risk of developing cerebral edema, which can have severe consequences. However, there are currently few effective tools for early identification or prediction of this risk. As machine learning (ML) is increasingly used in clinical practice, its effectiveness in predicting cerebral edema risk in stroke patients has been explored. Nonetheless, the lack of systematic evidence on its predictive value challenges the update of simple and user-friendly risk assessment tools. Therefore, we conducted a systematic review to evaluate the predictive utility of ML for cerebral edema in stroke patients.

METHODS

We searched PubMed, Embase, Web of Science, and the Cochrane Database up to February 21, 2024. The risk of bias in selected studies was assessed using a bias assessment tool for predictive models. Meta-analysis synthesized results from validation sets.

RESULTS

We included 22 studies with 25,096 stroke patients and 25 models, which were constructed using common and interpretable clinical features. In the validation cohort, the models achieved a concordance index (c-index) of 0.840 (95% CI: 0.810-0.871) for predicting poststroke cerebral edema, with a sensitivity of 0.76 (95% CI: 0.72-0.79) and a specificity of 0.87 (95% CI: 0.83-0.90).

CONCLUSION

ML models are significant in predicting poststroke cerebral edema, providing clinicians with a powerful prognostic tool. However, radiomics-based research was not included. We anticipate advancements in radiomics research to enhance the predictive power of ML for poststroke cerebral edema.

Mild Hypothermia Therapy Reduces the Incidence of Early Cerebral Herniation and Decompressive Craniectomy after Mechanical Thrombectomy for Acute Ischemic Stroke with Large Infarction

The application value of mechanical thrombectomy (MT) in acute large-vessel occlusion cerebral infarction has been confirmed, but considering the poor prognosis of large-core infarction (LCI), the current guidelines and practices are based on anterior circulation small-core infarction. Reducing the perioperative complications of thrombectomy in LCIs is the key to saving more patients previously considered unsuitable for thrombectomy. Patients with acute anterior circulation cerebral infarction who were admitted to Suining Central Hospital of Sichuan Province from January 2022 to December 2023 and whose Alberta Stroke Program Early Computed Tomography Score value was 3-5 (the score range was 0-10, and the lower the score was, the larger the infarct area) or whose infarct core volume was ≥70 mL and who received MT were enrolled consecutively. The patients were grouped based on whether they were treated with mild hypothermia (mild hypothermia treatment group vs. conventional treatment group). Patients who were evaluated preoperatively for large-core cerebral infarction and underwent mild hypothermia treatment were performed immediately after MT. The clinical data of the patients were collected. The primary outcome events were the incidence of cerebral hernia within one week after the operation and the rate of requiring decompressive craniectomy (%). The secondary outcome was the modified Rankin scale (mRS) score at 90 days (the score range was 0-6, and the higher the score was, the greater the degree of functional disability). A total of 64 patients were included. Twenty-nine patients were assigned to the mild hypothermia treatment group, and 35 patients were assigned to the conventional treatment group. There was no significant difference in the baseline data between the two groups. The proportions of cerebral hernia and the need for decompressive craniectomy within one week after the operation were significantly lower in the mild hypothermia treatment group than in the conventional treatment group (31% vs. 57.1%, odds ratio [OR] 0.338, 95% confidence interval [CI] 0.120-0.948; p = 0.037). The proportion of patients who underwent decompressive craniectomy in the mild hypothermia treatment group was significantly lower (13.8% vs. 42.8%, OR 0.213, 95% CI 0.061-0.745, p = 0.011). There was no significant difference in the mRS score between the two groups at 90 days (4.31 ± 1.75 vs. 4.48 ± 1.57, p = 0.456) or in the proportion of patients with a good prognosis (mRS 0-3) between the two groups (OR 0.569, 95% CI 0.18-1.793, p = 0.333). Mild hypothermia treatment can reduce the incidence of early cerebral hernia and the need for decompressive craniectomy in patients with acute large-core cerebral infarction after MT; this treatment can be used as an important adjuvant treatment after thrombectomy for LCI, but may not change the long-term prognosis.

Predictive Value of Plasma D-Dimer for Cerebral Herniation Post-Thrombectomy in Acute Ischemic Stroke Patients

Background

Cerebral hernia is a serious complication after thrombectomy in patients with acute ischemic stroke (AIS). This study aims to explore the predictive value of emergency preoperative plasma D-dimer levels in cerebral herniation after successful thrombectomy.

Methods

Between January 2019 and December 2023, patients with AIS who received a successful thrombectomy in our single comprehensive stroke center were retrospectively enrolled. We conducted a statistical analysis on the data using SPSS 26.0. Receiver operating characteristic curve (ROC) was used to evaluate the predictive value of D-dimer level for cerebral herniation.

Results

Among 278 enrolled patients, 20 cases (7.19%) experienced cerebral herniation. In patients with cerebral hernia, the score of the National Institutes of Health Stroke Scale was higher (16.5 vs 12.0, P < 0.001), the Alberta Stroke Plan early CT score was lower (6.5 vs 8.0, P < 0.001), the score of collateral circulation was lower (2.0 vs 3.0, P < 0.001), the proportion of eTICI blood flow grading of 3 of the occluded vessel was less (35% vs 75.19%), the proportion of pathogenesis of large atherosclerosis was less (5.00% vs 46.51%, P < 0.001), and the level of plasma D-dimer was higher (2.61 vs 0.82). After adjusting for potential confounders, the level of D-dimer (adjusted OR = 1.131, 95% CI 1.022-1.250, P = 0.017) was significantly correlated with cerebral hernia. Based on the ROC curve, the sensitivity and specificity of D-dimer in predicting cerebral herniation were 75.0% and 73.3%, respectively, and the area under the curve was 0.766.

Conclusion

Although our study had certain limitations, we found that elevated emergency preoperative plasma D-dimer level is an independent predictive factor for the cerebral herniation after successful thrombectomy in patients with AIS, which is of great clinical significance.

HELMET: A Hybrid Machine Learning Framework for Real-Time Prediction of Edema Trajectory in Large Middle Cerebral Artery Stroke

Malignant cerebral edema occurs when brain swelling displaces and compresses vital midline structures within the first week of a large middle cerebral artery stroke. Early interventions such as hyperosmolar therapy or surgical decompression may reverse secondary injury but must be administered judiciously. To optimize treatment and reduce secondary damage, clinicians need strategies to frequently and quantitatively assess the trajectory of edema using updated, relevant information. However, existing risk assessment tools are limited by the absence of structured records capturing the evolution of edema and typically estimate risk at a single time point early in the admission, therefore failing to account for changes in variables over the following hours or days. To address this, we developed and validated dynamic machine learning models capable of accurately predicting the severity of midline structure displacement, an established indicator of malignant edema, in real-time. Our models can provide updated estimations as frequently as every hour, using data from structured time-varying patient records, radiographic text, and human-curated neurological characteristics. Our work resulted in two novel multi-class classification models, collectively named Hybrid Ensemble Learning Models for Edema Trajectory (HELMET), predicting the progression of midline shift over 8-hour (HELMET-8) and 24-hour windows (HELMET-24), respectively. HELMET combines transformer-based large language models with supervised ensemble learning, demonstrating the value of merging human expertise and multimodal health records in developing clinical risk scores. Both models were trained on a retrospective cohort of 15,696 observations from 623 patients hospitalized with large middle cerebral artery ischemic stroke and were externally validated using 3,713 observations from 60 patients at a separate hospital system. Our HELMET models are accurate and generalize effectively to diverse populations, achieving a cross-validated mean area under the receiver operating characteristic score of 96.6% in the derivation cohort and 92.5% in the external validation cohort. Moreover, our approach provides a framework for developing hybrid risk prediction models that integrate both human-extracted and algorithm-derived multi-modal inputs. Our work enables accurate estimation of complex, dynamic, and highly specific clinical targets, such as midline shift, in real-time, even when relevant structured information is limited in electronic health record databases.

Development and validation of a preliminary multivariable diagnostic model for identifying unusual infections in hospitalized patients

Diagnostic delay leads to poor outcomes in infections, and it occurs more often when the causative agent is unusual. Delays are attributable to failing to consider such diagnoses in a timely fashion. Using routinely collected electronic health record (EHR) data, we built a preliminary multivariable diagnostic model for early identification of unusual fungal infections and tuberculosis in hospitalized patients. We conducted a two-gate case-control study. Cases encompassed adult patients admitted to 19 Mayo Clinic enterprise hospitals between January 2010 and March 2023 diagnosed with blastomycosis, cryptococcosis, histoplasmosis, mucormycosis, pneumocystosis, or tuberculosis. Control groups were drawn from all admitted patients (random controls) and those with community-acquired infections (ID-controls). Development and validation datasets were created using randomization for dividing cases and controls (7:3), with a secondary validation using ID-controls. A logistic regression model was constructed using baseline and laboratory variables, with the unusual infections of interest outcome. The derivation dataset comprised 1043 cases and 7000 random controls, while the 451 cases were compared to 3000 random controls and 1990 ID-controls for validation. Within the derivation dataset, the model achieved an area under the curve (AUC) of 0.88 (95% confidence interval [CI]: 0.87-0.89) with a good calibration accuracy (Hosmer-Lemeshow P = 0.623). Comparable performance was observed in the primary (AUC = 0.88; 95% CI: 0.86-0.9) and secondary validation datasets (AUC = 0.84; 95% CI: 0.82-0.86). In this multicenter study, an EHR-based preliminary diagnostic model accurately identified five unusual fungal infections and tuberculosis in hospitalized patients. With further validation, this model could help decrease time to diagnosis.

Enhanced Prediction of Malignant Cerebral Edema in Large Vessel Occlusion with Successful Recanalization Through Automated Weighted Net Water Uptake

BACKGROUND

Malignant cerebral edema (MCE) is associated with both net water uptake (NWU) and infarct volume. We hypothesized that NWU weighted by the affected Alberta Stroke Program Early Computed Tomography Score (ASPECTS) regions could serve as a quantitative imaging biomarker of aggravated edema development in acute ischemic stroke with large vessel occlusion (LVO). The aim of this study was to evaluate the performance of weighted NWU (wNWU) to predict MCE in patients with mechanical thrombectomy (MT).

METHODS

We retrospectively analyzed consecutive patients who underwent MT due to LVO. NWU was computed from nonenhanced computed tomography scans upon admission using automated ASPECTS software. wNWU was derived by multiplying NWU with the number of affected ASPECTS regions in the ischemic hemisphere. Predictors of MCE were assessed through multivariate logistic regression analysis and receiver operating characteristic curves.

RESULTS

NWU and wNWU were significantly higher in MCE patients than in non-MCE patients. Vessel recanalization status influenced the performance of wNWU in predicting MCE. In patients with successful recanalization, wNWU was an independent predictor of MCE (adjusted odds ratio 1.61; 95% confidence interval [CI] 1.24-2.09; P < 0.001). The model integrating wNWU, National Institutes of Health Stroke Scale, and collateral score exhibited an excellent performance in predicting MCE (area under the curve 0.80; 95% CI 0.75-0.84). Among patients with unsuccessful recanalization, wNWU did not influence the development of MCE (adjusted odds ratio 0.99; 95% CI 0.60-1.62; P = 0.953).

CONCLUSIONS

This study revealed that wNWU at admission can serve as a quantitative predictor of MCE in LVO with successful recanalization after MT and may contribute to the decision for early intervention.

Updates in Management of Large Hemispheric Infarct

This review delves into updates in management of large hemispheric infarction (LHI), a condition affecting up to 10% of patients with supratentorial strokes. While traditional management paradigms have endured, recent strides in research have revolutionized the approach to acute therapies, monitoring, and treatment. Notably, advancements in triage methodologies and the application of both pharmacological and mechanical abortive procedures have reshaped the acute care trajectory for patients with LHI. Moreover, ongoing endeavors have sought to refine strategies for the optimal surveillance and mitigation of complications, notably space-occupying mass effect, which can ensue in the aftermath of LHI. By amalgamating contemporary guidelines with cutting-edge clinical trial findings, this review offers a comprehensive exploration of the current landscape of acute and ongoing patient care for LHI, illuminating the evolving strategies that underpin effective management in this critical clinical domain.

Novel advanced imaging techniques for cerebral oedema

Cerebral oedema following acute ischemic infarction has been correlated with poor functional outcomes and is the driving mechanism of malignant infarction. Measurements of midline shift and qualitative assessment for herniation are currently the main CT indicators for cerebral oedema but have limited sensitivity for small cortical infarcts and are typically a delayed sign. In contrast, diffusion-weighted (DWI) or T2-weighted magnetic resonance imaging (MRI) are highly sensitive but are significantly less accessible. Due to the need for early quantification of cerebral oedema, several novel imaging biomarkers have been proposed. Based on neuroanatomical shift secondary to space-occupying oedema, measures such as relative hemispheric volume and cerebrospinal fluid displacement are correlated with poor outcomes. In contrast, other imaging biometrics, such as net water uptake, T2 relaxometry and blood brain barrier permeability, reflect intrinsic tissue changes from the influx of fluid into the ischemic region. This review aims to discuss quantification of cerebral oedema using current and developing advanced imaging techniques, and their role in predicting clinical outcomes.

Definition, prediction, prevention and management of patients with severe ischemic stroke and large infarction

ABSTRACT

Severe ischemic stroke carries a high rate of disability and death. The severity of stroke is often assessed by the degree of neurological deficits or the extent of brain infarct, defined as severe stroke and large infarction, respectively. Critically severe stroke is a life-threatening condition that requires neurocritical care or neurosurgical intervention, which includes stroke with malignant brain edema, a leading cause of death during the acute phase, and stroke with severe complications of other vital systems. Early prediction of high-risk patients with critically severe stroke would inform early prevention and treatment to interrupt the malignant course to fatal status. Selected patients with severe stroke could benefit from intravenous thrombolysis and endovascular treatment in improving functional outcome. There is insufficient evidence to inform dual antiplatelet therapy and the timing of anticoagulation initiation after severe stroke. Decompressive hemicraniectomy (DHC) <48 h improves survival in patients aged <60 years with large hemispheric infarction. Studies are ongoing to provide evidence to inform more precise prediction of malignant brain edema, optimal indications for acute reperfusion therapies and neurosurgery, and the individualized management of complications and secondary prevention. We present an evidence-based review for severe ischemic stroke, with the aims of proposing operational definitions, emphasizing the importance of early prediction and prevention of the evolution to critically severe status, summarizing specialized treatment for severe stroke, and proposing directions for future research.

Current and Emerging Endovascular and Neurocritical Care Management Strategies in Large-Core Ischemic Stroke

The volume of infarcted tissue in patients with ischemic stroke is consistently associated with increased morbidity and mortality. Initial studies of endovascular thrombectomy for large-vessel occlusion excluded patients with established large-core infarcts, even when large volumes of salvageable brain tissue were present, due to the high risk of hemorrhagic transformation and reperfusion injury. However, recent retrospective and prospective studies have shown improved outcomes with endovascular thrombectomy, and several clinical trials were recently published to evaluate the efficacy of endovascular management of patients presenting with large-core infarcts. With or without thrombectomy, patients with large-core infarcts remain at high risk of in-hospital complications such as hemorrhagic transformation, malignant cerebral edema, seizures, and others. Expert neurocritical care management is necessary to optimize blood pressure control, mitigate secondary brain injury, manage cerebral edema and elevated intracranial pressure, and implement various neuroprotective measures. Herein, we present an overview of the current and emerging evidence pertaining to endovascular treatment for large-core infarcts, recent advances in neurocritical care strategies, and their impact on optimizing patient outcomes.