Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients.

The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.

Temperature Control in Acute Brain Injury: An Update.

Temperature control in severe acute brain injury (SABI) is a key component of acute management. This manuscript delves into the complex role of temperature management in SABI, encompassing conditions like traumatic brain injury (TBI), acute ischemic stroke (AIS), intracerebral hemorrhage (ICH), aneurysmal subarachnoid hemorrhage (aSAH), and hypoxemic/ischemic brain injury following cardiac arrest. Fever is a common complication in SABI and is linked to worse neurological outcomes due to increased inflammatory responses and intracranial pressure (ICP). Temperature management, particularly hypothermic temperature control (HTC), appears to mitigate these adverse effects primarily by reducing cerebral metabolic demand and dampening inflammatory pathways. However, the effectiveness of HTC varies across different SABI conditions. In the context of post-cardiac arrest, the impact of HTC on neurological outcomes has shown inconsistent results. In cases of TBI, HTC seems promising for reducing ICP, but its influence on long-term outcomes remains uncertain. For AIS, clinical trials have yet to conclusively demonstrate the benefits of HTC, despite encouraging preclinical evidence. This variability in efficacy is also observed in ICH, aSAH, bacterial meningitis, and status epilepticus. In pediatric and neonatal populations, while HTC shows significant benefits in hypoxic-ischemic encephalopathy, its effectiveness in other brain injuries is mixed. Although the theoretical basis for employing temperature control, especially HTC, is strong, the clinical outcomes differ among various SABI subtypes. The current consensus indicates that fever prevention is beneficial across the board, but the application and effectiveness of HTC are more nuanced, underscoring the need for further research to establish optimal temperature management strategies. Here we provide an overview of the clinical evidence surrounding the use of temperature control in various types of SABI.

An Update on Curing Coma Campaign.

The Curing Coma Campaign (CCC) and its contributing collaborators identified multiple key areas of knowledge and research gaps in coma and disorders of consciousness (DoC). This step was a crucial effort and essential to prioritize future educational and research efforts. These key areas include defining categories of DoC, assessing DoC using multimodal approach (e.g., behavioral assessment tools, advanced neuroimaging studies), discussing optimal clinical trials' design and exploring computational models to conduct clinical trials in patients with DoC, and establishing common data elements to standardize data collection. Other key areas focused on creating coma care registry and educating clinicians and patients and promoting awareness of DoC to improve care in patients with DoC. The ongoing efforts in these key areas are discussed.

Artificial Intelligence Models for the Automation of Standard Diagnostics in Sleep Medicine-A Systematic Review.

Sleep disorders, prevalent in the general population, present significant health challenges. The current diagnostic approach, based on a manual analysis of overnight polysomnograms (PSGs), is costly and time-consuming. Artificial intelligence has emerged as a promising tool in this context, offering a more accessible and personalized approach to diagnosis, particularly beneficial for under-served populations. This is a systematic review of AI-based models for sleep disorder diagnostics that were trained, validated, and tested on diverse clinical datasets. An extensive search of PubMed and IEEE databases yielded 2114 articles, but only 18 met our stringent selection criteria, underscoring the scarcity of thoroughly validated AI models in sleep medicine. The findings emphasize the necessity of a rigorous validation of AI models on multimodal clinical data, a step crucial for their integration into clinical practice. This would be in line with the American Academy of Sleep Medicine's support of AI research.

Guidelines for Neuroprognostication in Critically Ill Adults with Moderate-Severe Traumatic Brain Injury.

BACKGROUND: Moderate-severe traumatic brain injury (msTBI) carries high morbidity and mortality worldwide. Accurate neuroprognostication is essential in guiding clinical decisions, including patient triage and transition to comfort measures. Here we provide recommendations regarding the reliability of major clinical predictors and prediction models commonly used in msTBI neuroprognostication, guiding clinicians in counseling surrogate decision-makers. METHODS: Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology, we conducted a systematic narrative review of the most clinically relevant predictors and prediction models cited in the literature. The review involved framing specific population/intervention/comparator/outcome/timing/setting (PICOTS) questions and employing stringent full-text screening criteria to examine the literature, focusing on four GRADE criteria: quality of evidence, desirability of outcomes, values and preferences, and resource use. Moreover, good practice recommendations addressing the key principles of neuroprognostication were drafted. RESULTS: After screening 8125 articles, 41 met our eligibility criteria. Ten clinical variables and nine grading scales were selected. Many articles varied in defining "poor" functional outcomes. For consistency, we treated "poor" as "unfavorable". Although many clinical variables are associated with poor outcome in msTBI, only the presence of bilateral pupillary nonreactivity on admission, conditional on accurate assessment without confounding from medications or injuries, was deemed moderately reliable for counseling surrogates regarding 6-month functional outcomes or in-hospital mortality. In terms of prediction models, the Corticosteroid Randomization After Significant Head Injury (CRASH)-basic, CRASH-CT (CRASH-basic extended by computed tomography features), International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT)-core, IMPACT-extended, and IMPACT-lab models were recommended as moderately reliable in predicting 14-day to 6-month mortality and functional outcomes at 6 months and beyond. When using "moderately reliable" predictors or prediction models, the clinician must acknowledge "substantial" uncertainty in the prognosis. CONCLUSIONS: These guidelines provide recommendations to clinicians on the formal reliability of individual predictors and prediction models of poor outcome when counseling surrogates of patients with msTBI and suggest broad principles of neuroprognostication.

MicroRNA Expression Profile in Acute Ischemic Stroke.

Introduction: Acute ischemic stroke with large vessel occlusion (LVO) continues to present a considerable challenge to global health, marked by substantial morbidity and mortality rates. Although definitive diagnostic markers exist in the form of neuroimaging, their expense, limited availability, and potential for diagnostic delay can often result in missed opportunities for life-saving interventions. Despite several past attempts, research efforts to date have been fraught with challenges likely due to multiple factors such as inclusion of diverse stroke types, variable onset intervals, differing pathobiologies, and a range of infarct sizes, all contributing to inconsistent circulating biomarker levels. In this context, microRNAs (miRNAs) have emerged as a promising biomarker, demonstrating potential as biomarkers across various diseases, including cancer, cardiovascular conditions, and neurological disorders. These circulating miRNAs embody a wide spectrum of pathophysiological processes, encompassing cell death, inflammation, angiogenesis, neuroprotection, brain plasticity, and blood-brain barrier integrity. This pilot study explores the utility of circulating exosome-enriched extracellular vesicle (EV) miRNAs as potential biomarkers for anterior circulation LVO (acLVO) stroke. Methods: In our longitudinal prospective cohort study, we collected data from acute large vessel occlusion (acLVO) stroke patients at four critical time intervals post-symptom onset: 0-6 hours, 6-12 hours, 12-24 hours, and 5-7 days. For comparative analysis, healthy individuals were included as control subjects. In this study, extracellular vesicles (EVs) were isolated from the plasma of participants, and the miRNAs within these EVs were profiled utilizing the NanoString nCounter system. Complementing this, a scoping review was conducted to examine the roles of specific miRNAs such as miR-140-5p, miR-210-3p, and miR-7-5p in acute ischemic stroke (AIS). This review involved a targeted PubMed search to assess their influence on crucial pathophysiological pathways in AIS, and their potential applications in diagnosis, treatment, and prognosis. The review also included an assessment of additional miRNAs linked to stroke. Results: Within the first 6 hours of symptom onset, three specific miRNAs (miR-7-5p, miR-140-5p, and miR-210-3p) exhibited significant differential expression compared to other time points and healthy controls. These miRNAs have previously been associated with neuroprotection, cellular stress responses, and tissue damage, suggesting their potential as early markers of acute ischemic stroke. Conclusion: This study highlights the potential of circulating miRNAs as blood-based biomarkers for hyperacute acLVO ischemic stroke. However, further validation in a larger, risk-matched cohort is required. Additionally, investigations are needed to assess the prognostic relevance of these miRNAs by linking their expression profiles with radiological and functional outcomes.

Guidelines for Neuroprognostication in Critically Ill Adults with Intracerebral Hemorrhage.

BACKGROUND: The objective of this document is to provide recommendations on the formal reliability of major clinical predictors often associated with intracerebral hemorrhage (ICH) neuroprognostication. METHODS: A narrative systematic review was completed using the Grading of Recommendations Assessment, Development, and Evaluation methodology and the Population, Intervention, Comparator, Outcome, Timing, Setting questions. Predictors, which included both individual clinical variables and prediction models, were selected based on clinical relevance and attention in the literature. Following construction of the evidence profile and summary of findings, recommendations were based on Grading of Recommendations Assessment, Development, and Evaluation criteria. Good practice statements addressed essential principles of neuroprognostication that could not be framed in the Population, Intervention, Comparator, Outcome, Timing, Setting format. RESULTS: Six candidate clinical variables and two clinical grading scales (the original ICH score and maximally treated ICH score) were selected for recommendation creation. A total of 347 articles out of 10,751 articles screened met our eligibility criteria. Consensus statements of good practice included deferring neuroprognostication-aside from the most clinically devastated patients-for at least the first 48-72 h of intensive care unit admission; understanding what outcomes would have been most valued by the patient; and counseling of patients and surrogates whose ultimate neurological recovery may occur over a variable period of time. Although many clinical variables and grading scales are associated with ICH poor outcome, no clinical variable alone or sole clinical grading scale was suggested by the panel as currently being reliable by itself for use in counseling patients with ICH and their surrogates, regarding functional outcome at 3 months and beyond or 30-day mortality. CONCLUSIONS: These guidelines provide recommendations on the formal reliability of predictors of poor outcome in the context of counseling patients with ICH and surrogates and suggest broad principles of neuroprognostication. Clinicians formulating their judgments of prognosis for patients with ICH should avoid anchoring bias based solely on any one clinical variable or published clinical grading scale.

Guidelines for neuroprognostication in adults with traumatic spinal cord injury.

BACKGROUND: Traumatic spinal cord injury (tSCI) impacts patients and their families acutely and often for the long term. The ability of clinicians to share prognostic information about mortality and functional outcomes allows patients and their surrogates to engage in decision-making and plan for the future. These guidelines provide recommendations on the reliability of acute-phase clinical predictors to inform neuroprognostication and guide clinicians in counseling adult patients with tSCI or their surrogates. METHODS: A narrative systematic review was completed using Grading of Recommendations Assessment, Development, and Evaluation methodology. Candidate predictors, including clinical variables and prediction models, were selected based on clinical relevance and presence of an appropriate body of evidence. The Population/Intervention/Comparator/Outcome/Timing/Setting question was framed as "When counseling patients or surrogates of critically ill patients with traumatic spinal cord injury, should < predictor, with time of assessment if appropriate > be considered a reliable predictor of < outcome, with time frame of assessment >?" Additional full-text screening criteria were used to exclude small and lower quality studies. Following construction of an evidence profile and summary of findings, recommendations were based on four Grading of Recommendations Assessment, Development, and Evaluation criteria: quality of evidence, balance of desirable and undesirable consequences, values and preferences, and resource use. Good practice recommendations addressed essential principles of neuroprognostication that could not be framed in the Population/Intervention/Comparator/Outcome/Timing/Setting format. Throughout the guideline development process, an individual living with tSCI provided perspective on patient-centered priorities. RESULTS: Six candidate clinical variables and one prediction model were selected. Out of 11,132 articles screened, 369 met inclusion criteria for full-text review and 35 articles met eligibility criteria to guide recommendations. We recommend pathologic findings on magnetic resonance imaging, neurological level of injury, and severity of injury as moderately reliable predictors of American Spinal Cord Injury Impairment Scale improvement and the Dutch Clinical Prediction Rule as a moderately reliable prediction model of independent ambulation at 1 year after injury. No other reliable or moderately reliable predictors of mortality or functional outcome were identified. Good practice recommendations include considering the complete clinical condition as opposed to a single variable and communicating the challenges of likely functional deficits as well as potential for improvement and for long-term quality of life with SCI-related deficits to patients and surrogates. CONCLUSIONS: These guidelines provide recommendations about the reliability of acute-phase predictors of mortality, functional outcome, American Spinal Injury Association Impairment Scale grade conversion, and recovery of independent ambulation for consideration when counseling patients with tSCI or their surrogates and suggest broad principles of neuroprognostication in this context.

Neurologic Complications of Cardiac and Pulmonary Disease.

OBJECTIVE: The heart and lungs work as a functional unit through a complex interplay. The cardiorespiratory system is responsible for the delivery of oxygen and energy substrates to the brain. Therefore, diseases of the heart and lungs can lead to various neurologic illnesses. This article reviews various cardiac and pulmonary pathologies that can lead to neurologic injury and discusses the relevant pathophysiologic mechanisms. LATEST DEVELOPMENTS: We have lived through unprecedented times over the past 3 years with the emergence and rapid spread of the COVID-19 pandemic. Given the effects of COVID-19 on the lungs and heart, an increased incidence of hypoxic-ischemic brain injury and stroke associated with cardiorespiratory pathologies has been observed. Newer evidence has questioned the benefit of induced hypothermia in patients with out-of-hospital cardiac arrest. Further, global collaborative initiatives such as the Curing Coma Campaign are underway with the goal of improving the care of patients with coma and disorders of consciousness, including those resulting from cardiac and pulmonary pathologies. ESSENTIAL POINTS: The neurologic complications of cardiorespiratory disorders are common and present in various forms such as stroke or hypoxic and anoxic injury related to cardiac or respiratory failure. With the emergence of the COVID-19 pandemic, neurologic complications have increased in recent years. Given the intimate and interdependent dynamics of the heart, lungs, and brain, it is crucial for neurologists to be aware of the interplay between these organs.

Multimodal and autoregulation monitoring in the neurointensive care unit.

Given the complexity of cerebral pathology in patients with acute brain injury, various neuromonitoring strategies have been developed to better appreciate physiologic relationships and potentially harmful derangements. There is ample evidence that bundling several neuromonitoring devices, termed "multimodal monitoring," is more beneficial compared to monitoring individual parameters as each may capture different and complementary aspects of cerebral physiology to provide a comprehensive picture that can help guide management. Furthermore, each modality has specific strengths and limitations that depend largely on spatiotemporal characteristics and complexity of the signal acquired. In this review we focus on the common clinical neuromonitoring techniques including intracranial pressure, brain tissue oxygenation, transcranial doppler and near-infrared spectroscopy with a focus on how each modality can also provide useful information about cerebral autoregulation capacity. Finally, we discuss the current evidence in using these modalities to support clinical decision making as well as potential insights into the future of advanced cerebral homeostatic assessments including neurovascular coupling.

Imaging biomarkers of cerebral edema automatically extracted from routine CT scans of large vessel occlusion strokes.

BACKGROUND AND PURPOSE: Volumetric and densitometric biomarkers have been proposed to better quantify cerebral edema after stroke, but their relative performance has not been rigorously evaluated. METHODS: Patients with large vessel occlusion stroke from three institutions were analyzed. An automated pipeline extracted brain, cerebrospinal fluid (CSF), and infarct volumes from serial CTs. Several biomarkers were measured: change in global CSF volume from baseline (ΔCSF); ratio of CSF volumes between hemispheres (CSF ratio); and relative density of infarct region compared with mirrored contralateral region (net water uptake [NWU]). These were compared to radiographic standards, midline shift and relative hemispheric volume (RHV) and malignant edema, defined as deterioration resulting in need for osmotic therapy, decompressive surgery, or death. RESULTS: We analyzed 255 patients with 210 baseline CTs, 255 24-hour CTs, and 81 72-hour CTs. Of these, 35 (14%) developed malignant edema and 63 (27%) midline shift. CSF metrics could be calculated for 310 (92%), while NWU could only be obtained from 193 (57%). Peak midline shift was correlated with baseline CSF ratio (ρ = -.22) and with CSF ratio and ΔCSF at 24 hours (ρ = -.55/.63) and 72 hours (ρ = -.66/.69), but not with NWU (ρ = .15/.25). Similarly, CSF ratio was correlated with RHV (ρ = -.69/-.78), while NWU was not. Adjusting for age, National Institutes of Health Stroke Scale, tissue plasminogen activator treatment, and Alberta Stroke Program Early CT Score, CSF ratio (odds ratio [OR]: 1.95 per 0.1, 95% confidence interval [CI]: 1.52-2.59) and ΔCSF at 24 hours (OR: 1.87 per 10%, 95% CI: 1.47-2.49) were associated with malignant edema. CONCLUSION: CSF volumetric biomarkers can be automatically measured from almost all routine CTs and correlate better with standard edema endpoints than net water uptake.

Guidelines for Neuroprognostication in Adults with Guillain-Barré Syndrome.

BACKGROUND: Guillain-Barré syndrome (GBS) often carries a favorable prognosis. Of adult patients with GBS, 10-30% require mechanical ventilation during the acute phase of the disease. After the acute phase, the focus shifts to restoration of motor strength, ambulation, and neurological function, with variable speed and degree of recovery. The objective of these guidelines is to provide recommendations on the reliability of select clinical predictors that serve as the basis of neuroprognostication and provide guidance to clinicians counseling adult patients with GBS and/or their surrogates. METHODS: A narrative systematic review was completed using Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. Candidate predictors, including clinical variables and prediction models, were selected based on clinical relevance and presence of appropriate body of evidence. The Population/Intervention/Comparator/Outcome/Time frame/Setting (PICOTS) question was framed as follows: "When counseling patients or surrogates of critically ill patients with Guillain-Barré syndrome, should [predictor, with time of assessment if appropriate] be considered a reliable predictor of [outcome, with time frame of assessment]?" Additional full-text screening criteria were used to exclude small and lower quality studies. Following construction of an evidence profile and summary of findings, recommendations were based on four GRADE criteria: quality of evidence, balance of desirable and undesirable consequences, values and preferences, and resource use. In addition, good practice recommendations addressed essential principles of neuroprognostication that could not be framed in PICOTS format. RESULTS: Eight candidate clinical variables and six prediction models were selected. A total of 45 articles met our eligibility criteria to guide recommendations. We recommend bulbar weakness (the degree of motor weakness at disease nadir) and the Erasmus GBS Respiratory Insufficiency Score as moderately reliable for prediction of the need for mechanical ventilation. The Erasmus GBS Outcome Score (EGOS) and modified EGOS were identified as moderately reliable predictors of independent ambulation at 3 months and beyond. Good practice recommendations include consideration of both acute and recovery phases of the disease during prognostication, discussion of the possible need for mechanical ventilation and enteral nutrition during counseling, and consideration of the complete clinical condition as opposed to a single variable during prognostication. CONCLUSIONS: These guidelines provide recommendations on the reliability of predictors of the need for mechanical ventilation, poor functional outcome, and independent ambulation following GBS in the context of counseling patients and/or surrogates and suggest broad principles of neuroprognostication. Few predictors were considered moderately reliable based on the available body of evidence, and higher quality data are needed.

Guidelines for Neuroprognostication in Comatose Adult Survivors of Cardiac Arrest.

BACKGROUND: Among cardiac arrest survivors, about half remain comatose 72 h following return of spontaneous circulation (ROSC). Prognostication of poor neurological outcome in this population may result in withdrawal of life-sustaining therapy and death. The objective of this article is to provide recommendations on the reliability of select clinical predictors that serve as the basis of neuroprognostication and provide guidance to clinicians counseling surrogates of comatose cardiac arrest survivors. METHODS: A narrative systematic review was completed using Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. Candidate predictors, which included clinical variables and prediction models, were selected based on clinical relevance and the presence of an appropriate body of evidence. The Population, Intervention, Comparator, Outcome, Timing, Setting (PICOTS) question was framed as follows: "When counseling surrogates of comatose adult survivors of cardiac arrest, should [predictor, with time of assessment if appropriate] be considered a reliable predictor of poor functional outcome assessed at 3 months or later?" Additional full-text screening criteria were used to exclude small and lower-quality studies. Following construction of the evidence profile and summary of findings, recommendations were based on four GRADE criteria: quality of evidence, balance of desirable and undesirable consequences, values and preferences, and resource use. In addition, good practice recommendations addressed essential principles of neuroprognostication that could not be framed in PICOTS format. RESULTS: Eleven candidate clinical variables and three prediction models were selected based on clinical relevance and the presence of an appropriate body of literature. A total of 72 articles met our eligibility criteria to guide recommendations. Good practice recommendations include waiting 72 h following ROSC/rewarming prior to neuroprognostication, avoiding sedation or other confounders, the use of multimodal assessment, and an extended period of observation for awakening in patients with an indeterminate prognosis, if consistent with goals of care. The bilateral absence of pupillary light response > 72 h from ROSC and the bilateral absence of N20 response on somatosensory evoked potential testing were identified as reliable predictors. Computed tomography or magnetic resonance imaging of the brain > 48 h from ROSC and electroencephalography > 72 h from ROSC were identified as moderately reliable predictors. CONCLUSIONS: These guidelines provide recommendations on the reliability of predictors of poor outcome in the context of counseling surrogates of comatose survivors of cardiac arrest and suggest broad principles of neuroprognostication. Few predictors were considered reliable or moderately reliable based on the available body of evidence.

Artificial Intelligence and Big Data Science in Neurocritical Care.

In recent years, the volume of digitalized web-based information utilizing modern computer-based technology for data storage, processing, and analysis has grown rapidly. Humans can process a limited number of variables at any given time. Thus, the deluge of clinically useful information in the intensive care unit environment remains untapped. Innovations in machine learning technology with the development of deep neural networks and efficient, cost-effective data archival systems have provided the infrastructure to apply artificial intelligence on big data for determination of clinical events and outcomes. Here, we introduce a few computer-based technologies that have been tested across these domains.

The Curing Coma Campaign and the Future of Coma Research.

Recovery from coma or disordered consciousness is a central issue in patients with acute brain injuries such as stroke, trauma, cardiac arrest, and brain infections. Yet, major gaps remain in the scientific underpinnings of coma and this has led to inaccuracy in prognostication and limited interventions for coma recovery. Even so, recent studies have begun to elucidate mechanisms of consciousness early and prolonged after acute brain injury and some pilot interventions have begun to be tested. The importance and scope of this led in 2019 to the development of the Curing Coma Campaign, an initiative of the Neurocritical Care Society designed to provide a platform for scientific collaboration across the patient care continuum and to empower a community for purposes of research, education, implementation science, and advocacy. Seen as a "grand challenge," the Curing Coma Campaign has developed an infrastructure of scientific working groups and operational modules, along with a 10-year roadmap.