Multimodal and autoregulation monitoring in the neurointensive care unit

Individualized autoregulation-guided arterial blood pressure management in neurocritical care

Cerebral autoregulation (CA) is the physiological process by which cerebral blood flow is maintained during fluctuations in arterial blood pressure (ABP). There are various validated methods to measure CA, either invasively, with intracranial pressure or brain tissue oxygenation monitors, or noninvasively, with transcranial Doppler ultrasound or near-infrared spectroscopy. Utilizing these monitors, researchers have been able to discern CA patterns in several pathological states, such as but not limited to acute ischemic stroke, spontaneous intracranial hemorrhage, aneurysmal subarachnoid hemorrhage, sepsis, and post-cardiac arrest, and they have found CA to be altered in these patients. CA disturbances predispose patients suffering from these ailments to worse outcomes. Much focus has been placed on CA monitoring in these populations, with an emphasis on arterial blood pressure optimization. Many guidelines recommend universal static ABP targets; however, in patients with altered CA, these targets may make them susceptible to hypoperfusion and further neurological injury. Based on this observation, there has been much investigation on individualized ABP goals and their effect on clinical outcomes. The scope of this review includes (1) a summary of the physiology of CA in healthy adults; (2) a review of the evidence on CA monitoring in healthy individuals; (3) a summary of CA changes and its effect on outcomes in various diseased states including acute ischemic stroke, spontaneous intracranial hemorrhage, aneurysmal subarachnoid hemorrhage, sepsis and meningitis, post-cardiac arrest, hypoxic-ischemic encephalopathy, surgery, and moyamoya disease; and (4) a review of the current evidence on individualized ABP changes in various patient populations.

Brain health: A concern for anaesthesiologists and intensivists

Damage to the brain can have disastrous and long-lasting consequences. The European Society of Anaesthesiology and Intensive Care (ESAIC) is aware of the importance of taking good care of the brain, both of patients and of anaesthesia and intensive care unit (ICU) caregivers, and has organised a complete learning track on brain health to bring this concern to the attention of practitioners. This learning track included an online Focus Meeting on Brain Health (November 25, 2023). We here provide readers with a digest of the information that was delivered during that meeting in an opinion paper driven by the authors' own reading of the literature. It is divided according to the meeting's sessions, including how to improve the health of an injured brain, how to keep a young or old brain healthy, how to keep a healthy adult brain unimpaired, how monitoring can impact brain health in the operating room and in the intensive care unit, and how to keep the anaesthesia and ICU caregivers' brain healthy. Each part is a brief and focused summary. The main delivered messages are that the management of injured brain patients involves an adequate choice of sedation, adequate brain monitoring, and focused attention to specific points depending on the underlying pathology; that several measures can be undertaken to protect the brain of the very young needing anaesthesia; that it is possible to detect older patients at risk of postoperative neurocognitive disorders, and that dedicated perioperative management by a multidisciplinary expert team may improve their outcomes; that apparently healthy adult brains may suffer during anaesthesia; that the electroencephalogram may track peri-operative brain dysfunction, and that female patients should be given special care in this respect; that multimodal brain monitoring helps to detect pathological processes and to maintain brain homeostasis; and that burnout in anaesthesiologists can be effectively fought using personal, organisational, managerial and legal approaches.

Method for detection of cerebral blood flow in neurointensive care using longitudinal arterial spin labeling MRI

Cerebral blood flow (CBF) is carefully monitored in the Neurointensive Care Unit (NICU) to prevent secondary brain insults in patients who have suffered subarachnoid hemorrhage. Including absolute MRI measurements of CBF in the NICU monitoring protocol could add valuable information and potentially improve patient outcomes. This is particularly feasible at Linköping University Hospital, which uniquely has an MRI scanner located in the NICU, enabling longitudinal CBF measurements while eliminating medical transportation risks. Arterial spin labeling is a subtraction-based MRI technique that can measure CBF globally in the brain without the use of contrast agents, and thus is suitable for repeated measurements over short time periods. Therefore, this work aims to develop and implement a methodological workflow for the acquisition, analysis, absolute quantification, and visualization of longitudinal arterial spin labeling MRI measurements acquired in the clinical NICU setting. At this initial stage, the workflow was implemented and tested using acquired test-retest data and longitudinal data from two healthy participants. Subsequently, the workflow was tested in clinical practice on an intubated and ventilated patient monitored in the NICU after suffering a subarachnoid hemorrhage. To ensure accurate day-to-day comparisons between the repeated measurements, the selection of processing and analysis methods aimed to obtain CBF maps in absolute units of ml/min/100g. These CBF maps were quantified using both the FMRIB Software Library and an openly available flow territory atlas. The test-retest data showed small variations (4.4 ml/min/100g between sessions), and the longitudinal measurement resulted in low CBF variability over 12 days. Despite the greater complexity of clinical data, the quantification and chosen visualization tools proved helpful in interpreting the results. In conclusion, this workflow including repeated MRI measurements could help detect changes in CBF between different measurement days and complement other conventional monitoring techniques in the NICU.

Monitoring of Cerebral Blood Flow Autoregulation after Cardiac Arrest

Background: Cardiac arrest remains one of the leading causes of death. After successful resuscitation of patients in cardiac arrest, post-cardiac arrest syndrome develops, part of it being an impaired cerebral blood flow autoregulation. Monitoring cerebral blood flow autoregulation after cardiac arrest is important for optimizing patient care and prognosticating patients' survival, yet remains a challenge. There are still gaps in clinical implications and everyday use. In this article, we present a systematic review of studies with different methods of monitoring cerebral blood flow autoregulation after non-traumatic cardiac arrest. Methods: A comprehensive literature search was performed from 1 June 2024 to 27 June 2024 by using multiple databases: PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials. Inclusion criteria were studies with an included description of the measurement of cerebral blood flow autoregulation in adult patients after non-traumatic cardiac arrest. Results: A total of 16 studies met inclusion criteria. Our data show that the most used methods in the reviewed studies were near-infrared spectroscopy and transcranial Doppler. The most used mathematical methods for calculating cerebral autoregulation were cerebral oximetry index, tissue oxygenation reactivity index, and mean flow index. Conclusions: The use of various monitoring and mathematical methods for calculating cerebral blood flow autoregulation poses a challenge for standardization, validation, and daily use in clinical practice. In the future studies, focus should be considered on clinical validation and transitioning autoregulation monitoring techniques to everyday clinical practice, which could improve the survival outcomes of patients after cardiac arrest.

Update in Pediatric Neurocritical Care: What a Neurologist Caring for Critically Ill Children Needs to Know

Currently nearly one-quarter of admissions to pediatric intensive care units (PICUs) worldwide are for neurocritical care diagnoses that are associated with significant morbidity and mortality. Pediatric neurocritical care is a rapidly evolving field with unique challenges due to not only age-related responses to primary neurologic insults and their treatments but also the rarity of pediatric neurocritical care conditions at any given institution. The structure of pediatric neurocritical care services therefore is most commonly a collaborative model where critical care medicine physicians coordinate care and are supported by a multidisciplinary team of pediatric subspecialists, including neurologists. While pediatric neurocritical care lies at the intersection between critical care and the neurosciences, this narrative review focuses on the most common clinical scenarios encountered by pediatric neurologists as consultants in the PICU and synthesizes the recent evidence, best practices, and ongoing research in these cases. We provide an in-depth review of (1) the evaluation and management of abnormal movements (seizures/status epilepticus and status dystonicus); (2) acute weakness and paralysis (focusing on pediatric stroke and select pediatric neuroimmune conditions); (3) neuromonitoring modalities using a pathophysiology-driven approach; (4) neuroprotective strategies for which there is evidence (e.g., pediatric severe traumatic brain injury, post-cardiac arrest care, and ischemic stroke and hemorrhagic stroke); and (5) best practices for neuroprognostication in pediatric traumatic brain injury, cardiac arrest, and disorders of consciousness, with highlights of the 2023 updates on Brain Death/Death by Neurological Criteria. Our review of the current state of pediatric neurocritical care from the viewpoint of what a pediatric neurologist in the PICU needs to know is intended to improve knowledge for providers at the bedside with the goal of better patient care and outcomes.

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.

Myths and methodologies: Assessment of dynamic cerebral autoregulation by the mean flow index

The mean flow index-usually referred to as Mx-has been used for assessing dynamic cerebral autoregulation (dCA) for almost 30 years. However, concerns have arisen regarding methodological consistency, construct and criterion validity, and test-retest reliability. Methodological nuances, such as choice of input (cerebral perfusion pressure, invasive or non-invasive arterial pressure), pre-processing approach and artefact handling, significantly influence mean flow index values, and previous studies correlating mean flow index with other established dCA metrics are confounded by inherent methodological flaws like heteroscedasticity, while the mean flow index also fails to discriminate individuals with presumed intact versus impaired dCA (discriminatory validity), and its prognostic performance (predictive validity) across various conditions remains inconsistent. The test-retest reliability, both within and between days, is generally poor. At present, no single approach for data collection or pre-processing has proven superior for obtaining the mean flow index, and caution is advised in the further use of mean flow index-based measures for assessing dCA, as current evidence does not support their clinical application.