Quantitative evaluation of electrographic response to electroconvulsive therapy in super-refractory status epilepticus.

Objective: Electroconvulsive therapy (ECT) has been occasionally applied as a treatment for super-refractory status epilepticus (SRSE). However, the effects of ECT on electrographic activity and related clinical outcomes are largely unknown. Here, we use quantitative approaches on electroencephalography (EEG) data to evaluate the neurophysiological influences of ECT and how they may relate to patient survival.

Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

Objective: This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalopathy (HIE) utilizing clinical and quantitative electroencephalogram (QEEG) features.

Methods: We developed a gradient boosting ML model (Neo-GB) that utilizes clinical features and QEEG to forecast time-dependent seizure risk. Clinical variables included cord blood gas values, Apgar scores, gestational age at birth, postmenstrual age (PMA), postnatal age, and birth weight.

Prevalence and Determinants of Prehospital Impression of Stroke in Ischemic Stroke Patients.

Background: Emergency Medical Services (EMS) clinicians are front-line in evaluating patients with stroke in the community. Their ability to correctly identify stroke influences downstream management decisions. We sought to use a large national database of prehospital clinical data to determine risk factors associated with missed EMS stroke identification.

Evaluating the feasibility of prehospital point-of-care EEG: The prehospital implementation of rapid EEG (PHIRE) study.

Background: Point-of-care electroencephalography (EEG) devices can be rapidly applied and do not require specialized technologists, creating new opportunities to use EEG during prehospital care. We evaluated the feasibility of point-of-care EEG during ambulance transport for 911 calls.

Methods: This mixed-methods study was conducted between May 28, 2022 and October 28, 2023.

Early Burst Suppression Similarity Association with Structural Brain Injury Severity on MRI After Cardiac Arrest.

Background: Identical bursts on electroencephalography (EEG) are considered a specific predictor of poor outcomes in cardiac arrest, but its relationship with structural brain injury severity on magnetic resonance imaging (MRI) is not known.

Methods: This was a retrospective analysis of clinical, EEG, and MRI data from adult comatose patients after cardiac arrest. Burst similarity in first 72 h from the time of return of spontaneous circulation were calculated using dynamic time-warping (DTW) for bursts of equal (i.

Small RNA signatures of acute ischemic stroke in L1CAM positive extracellular vesicles.

L1CAM-positive extracellular vesicles (L1EV) are an emerging biomarker that may better reflect ongoing neuronal damage than other blood-based biomarkers. The physiological roles and regulation of L1EVs and their small RNA cargoes following stroke is unknown. We sought to characterize L1EV small RNAs following stroke and assess L1EV RNA signatures for diagnosing stroke using weighted gene co-expression network analysis and random forest (RF) machine learning algorithms.

Mortality and Timing of Withdrawal of Life-Sustaining Therapies After Out-of-Hospital Cardiac Arrest: Two-Center Retrospective Pediatric Cohort Study.

Objectives: Pediatric out-of-hospital cardiac arrest (OHCA) is associated with substantial morbidity and mortality. Limited data exist to guide timing and method of neurologic prognostication after pediatric OHCA, making counseling on withdrawal of life-sustaining therapies (WLSTs) challenging. This study investigates the timing and mode of death after pediatric OHCA and factors associated with mortality.

A quantitative EEG index for the recognition of arterial ischemic stroke in children.

Objective: To describe and assess performance of the Correlate Of Injury to the Nervous system (COIN) index, a quantitative electroencephalography (EEG) metric designed to identify areas of cerebral dysfunction concerning for stroke.

Methods: Case-control study comparing continuous EEG data from children with acute ischemic stroke to children without stroke, with or without encephalopathy. COIN is calculated continuously and compares EEG power between cerebral hemispheres.

Correction: Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.

[This corrects the article DOI: 10.1371/journal.pone.

The International Cardiac Arrest Research Consortium Electroencephalography Database.

Objectives: To develop the International Cardiac Arrest Research (I-CARE), a harmonized multicenter clinical and electroencephalography database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest.

Design: Multicenter cohort, partly prospective and partly retrospective.

Setting: Seven academic or teaching hospitals from the United States and Europe.

Increased task-relevant fMRI responsiveness in comatose cardiac arrest patients is associated with improved neurologic outcomes.

Early prediction of the recovery of consciousness in comatose cardiac arrest patients remains challenging. We prospectively studied task-relevant fMRI responses in 19 comatose cardiac arrest patients and five healthy controls to assess the fMRI's utility for neuroprognostication. Tasks involved instrumental music listening, forward and backward language listening, and motor imagery.

The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database.

Objective: To develop a harmonized multicenter clinical and electroencephalography (EEG) database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest.

Design: Multicenter cohort, partly prospective and partly retrospective.

Setting: Seven academic or teaching hospitals from the U.

Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest.

Background And Objectives: Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury after cardiac arrest. We aimed to delineate the evolution of coma neurophysiology feature ensembles associated with recovery from coma after cardiac arrest.

Methods: Adults in acute coma after cardiac arrest were included in a retrospective database involving 7 hospitals.

Post-cardiac arrest Sedation Promotes Electroencephalographic Slow-wave Activity and Improves Survival in a Mouse Model of Cardiac Arrest.

Background: Patients resuscitated from cardiac arrest are routinely sedated during targeted temperature management, while the effects of sedation on cerebral physiology and outcomes after cardiac arrest remain to be determined. The authors hypothesized that sedation would improve survival and neurologic outcomes in mice after cardiac arrest.

Methods: Adult C57BL/6J mice of both sexes were subjected to potassium chloride-induced cardiac arrest and cardiopulmonary resuscitation.

High incidence of epileptiform activity in adults undergoing extracorporeal membrane oxygenation.

Objective: The prevalence of seizures and other types of epileptiform brain activity in patients undergoing extracorporeal membrane oxygenation (ECMO) is unknown. We aimed to estimate the prevalence of seizures and ictal-interictal continuum patterns in patients undergoing electroencephalography (EEG) during ECMO.

Methods: Retrospective review of a prospective ECMO registry from 2011-2018 in a university-affiliated academic hospital.

Neurocritical Care Performance Measures Derived from Electronic Health Record Data are Feasible and Reveal Site-Specific Variation: A CHoRUS Pilot Project.

Background: We evaluated the feasibility and discriminability of recently proposed Clinical Performance Measures for Neurocritical Care (Neurocritical Care Society) and Quality Indicators for Traumatic Brain Injury (Collaborative European NeuroTrauma Effectiveness Research in TBI; CENTER-TBI) extracted from electronic health record (EHR) flowsheet data.

Methods: At three centers within the Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI consortium, we examined consecutive neurocritical care admissions exceeding 24 h (03/2015-02/2020) and evaluated the feasibility, discriminability, and site-specific variation of five clinical performance measures and quality indicators: (1) intracranial pressure (ICP) monitoring (ICPM) within 24 h when indicated, (2) ICPM latency when initiated within 24 h, (3) frequency of nurse-documented neurologic assessments, (4) intermittent pneumatic compression device (IPCd) initiation within 24 h, and (5) latency to IPCd application. We additionally explored associations between delayed IPCd initiation and codes for venous thromboembolism documented using the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) system.

Geometric and Morphologic Features of Ruptured Intracranial Aneurysms Associated with Methamphetamine Use.

Background: Methamphetamine (MA) use is associated with poor outcomes after aneurysmal subarachnoid hemorrhage (aSAH). MA exerts both hemodynamic and inflammatory effects, but whether these manifest with altered intracranial aneurysm (IA) remodeling is unknown. The objective of this study was to compare IA geometric and morphologic features in patients with and without MA detected on urine toxicology (Utox) at presentation.

Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.

Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated machine learning (AutoML) in particular is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed. However, successful translation of AI/ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards establishing reproducible clinical and biological inferences.

Perceptions of Critical Care Shortages, Resource Use, and Provider Well-being During the COVID-19 Pandemic: A Survey of 1,985 Health Care Providers in Brazil.

Background: Brazil has been disproportionately affected by COVID-19, placing a high burden on ICUs.

Research Question: Are perceptions of ICU resource availability associated with end-of-life decisions and burnout among health care providers (HCPs) during COVID-19 surges in Brazil?

Study Design And Methods: We electronically administered a survey to multidisciplinary ICU HCPs during two 2-week periods (in June 2020 and March 2021) coinciding with COVID-19 surges. We examined responses across geographical regions and performed multivariate regressions to explore factors associated with reports of: (1) families being allowed less input in decisions about maintaining life-sustaining treatments for patients with COVID-19 and (2) emotional distress and burnout.