Corrigendum: The predictive validity of a Brain Care Score for dementia and stroke: data from the UK Biobank cohort.

[This corrects the article DOI: 10.3389/fneur.2023.

Prediction of Postoperative Delirium in Older Adults from Preoperative Cognition and Occipital Alpha Power from Resting-State Electroencephalogram.

Background: Postoperative delirium is the most common complication following surgery among older adults, and has been consistently associated with increased mortality and morbidity, cognitive decline, and loss of independence, as well as markedly increased health-care costs. Electroencephalography (EEG) spectral slowing has frequently been observed during episodes of delirium, whereas intraoperative frontal alpha power is associated with postoperative delirium. We sought to identify preoperative predictors that could identify individuals at high risk for postoperative delirium, which could guide clinical decision-making and enable targeted interventions to potentially decrease delirium incidence and postoperative delirium-related complications.

Evaluating the Impact of Point-of-Care Electroencephalography on Length of Stay in the Intensive Care Unit: Subanalysis of the SAFER-EEG Trial.

Background: Electroencephalography (EEG) is needed to diagnose nonconvulsive seizures. Prolonged nonconvulsive seizures are associated with neuronal injuries and deleterious clinical outcomes. However, it is uncertain whether the rapid identification of these seizures using point-of-care EEG (POC-EEG) can have a positive impact on clinical outcomes.

Seizure Assessment and Forecasting With Efficient Rapid-EEG: A Retrospective Multicenter Comparative Effectiveness Study.

Background And Objectives: Approximately 30% of critically ill patients have seizures, and more than half of these seizures do not have an overt clinical correlate. EEG is needed to avoid missing seizures and prevent overtreatment with antiseizure medications. Conventional-EEG (cEEG) resources are logistically constrained and unable to meet their growing demand for seizure detection even in highly developed centers.

The predictive validity of a Brain Care Score for dementia and stroke: data from the UK Biobank cohort.

Introduction: The 21-point Brain Care Score (BCS) was developed through a modified Delphi process in partnership with practitioners and patients to promote behavior changes and lifestyle choices in order to sustainably reduce the risk of dementia and stroke. We aimed to assess the associations of the BCS with risk of incident dementia and stroke.

Methods: The BCS was derived from the United Kingdom Biobank (UKB) baseline evaluation for participants aged 40-69 years, recruited between 2006-2010.

Self-Directed Home-Based Dim-Light Melatonin Onset Collection: The Circadia Pilot Study.

Study Objectives: To test the feasibility of a novel at-home salivary Dim Light Melatonin Onset (DLMO) assessment protocol to measure the endogenous circadian phase of 10 individuals ( 1 Advanced Sleep-Wake Phase Disorder patient (ASWPD), 4 Delayed Sleep-Wake Phase Disorder patients (DSWPD), and 5 controls).

Methods: The study tracked the sleep and activity patterns of 10 individuals over a 5-6 week period using self-reported online sleep diaries and objective actigraphy data. Participants completed two self-directed DLMO assessments, approximately one week apart, adhering to objective compliance measures.

A machine learning approach to predict progression on active surveillance for prostate cancer.

Purpose: Robust prediction of progression on active surveillance (AS) for prostate cancer can allow for risk-adapted protocols. To date, models predicting progression on AS have invariably used traditional statistical approaches. We sought to evaluate whether a machine learning (ML) approach could improve prediction of progression on AS.

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free versus IED EEGs. In this study, we evaluate features that may provide reliable IED detection and EEG classification.

Postoperative delirium mediates 180-day mortality in orthopaedic trauma patients.

Background: Frailty has been associated with increased incidence of postoperative delirium and mortality. We hypothesised that postoperative delirium mediates a clinically significant (≥1%) percentage of the effect of frailty on mortality in older orthopaedic trauma patients.

Methods: This was a single-centre, retrospective observational study including 558 adults 65 yr and older, who presented with an extremity fracture requiring hospitalisation without initial ICU admission.

Automated Adult Epilepsy Diagnostic Tool Based on Interictal Scalp Electroencephalogram Characteristics: A Six-Center Study.

The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan.

Clinical, Imaging, and Lab Correlates of Severe COVID-19 Leukoencephalopathy.

Background And Purpose: Patients infected with the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) can develop a spectrum of neurological disorders, including a leukoencephalopathy of variable severity. Our aim was to characterize imaging, lab, and clinical correlates of severe coronavirus disease 2019 (COVID-19) leukoencephalopathy, which may provide insight into the SARS-CoV-2 pathophysiology.

Materials And Methods: Twenty-seven consecutive patients positive for SARS-CoV-2 who had brain MR imaging following intensive care unit admission were included.

Deep Learning for Interictal Epileptiform Spike Detection from scalp EEG frequency sub bands.

Epilepsy diagnosis through visual examination of interictal epileptiform discharges (IEDs) in scalp electroencephalogram (EEG) signals is a challenging problem. Deep learning methods can be an automated way to perform this task. In this work, we present a new approach based on convolutional neural network (CNN) to detect IEDs from EEGs automatically.

Development and Prospective Validation of a Transparent Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.

Importance: Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation is of great importance and may aid in delivering timely treatment.

Objective: To develop, externally validate and prospectively test a transparent deep learning algorithm for predicting 24 hours in advance the need for mechanical ventilation in hospitalized patients and those with COVID-19.

Design: Observational cohort study SETTING: Two academic medical centers from January 01, 2016 to December 31, 2019 (Retrospective cohorts) and February 10, 2020 to May 4, 2020 (Prospective cohorts).

Development and Validation of Forecasting Next Reported Seizure Using e-Diaries.

Objective: There are no validated methods for predicting the timing of seizures. Using machine learning, we sought to forecast 24-hour risk of self-reported seizure from e-diaries.

Methods: Data from 5,419 patients on SeizureTracker.

Continuous EEG Findings in Autoimmune Encephalitis.

Purpose: Autoimmune encephalitis (AE) is a cause of new-onset seizures, including new-onset refractory status epilepticus, yet there have been few studies assessing the EEG signature of AE.

Methods: Multicenter retrospective review of patients diagnosed with AE who underwent continuous EEG monitoring.

Results: We identified 64 patients (male, 39%; white, 49%; median age, 44 years); of whom, 43 (67%) were antibody-proven AE patients.

Three-Dimensional Custom-Root Replicate Tooth Dental Implants.

This article summarizes the accomplishments and knowledge gained over the past 2 decades with respect to immediate dental root analogue implants (RAIs). It discusses how the artificial nature of the present dental implant materials and unnatural shapes cause complications, posing a threat to long-term biointegration, and how RAIs will influence the way that implants are produced. Will an osseointegrated RAI be the optimal immediate replacement for extracted teeth in the future? How will three-dimensional printing be involved in these more biomimetic RAI systems? The present research and developments seem promising and will continue to shape the future of implant prosthodontics.

Brain Monitoring of Sedation in the Intensive Care Unit Using a Recurrent Neural Network.

Over and under-sedation are common in critically ill patients admitted to the Intensive Care Unit. Clinical assessments provide limited time resolution and are based on behavior rather than the brain itself. Existing brain monitors have been developed primarily for non-ICU settings.

Neuroimaging Correlates of Periodic Discharges.

Despite being first described over 50 years ago, periodic discharges continue to generate controversy as to whether they are always, sometimes, or never "ictal." Investigators and clinicians have proposed adjunctive markers to help clarify this distinction-in particular measures of perfusion and metabolism. Here, we review the growing number of neuroimaging studies using Fluorodeoxyglucose-PET, MRI diffusion, Magnetic resonance perfusion, Single Photon Emission Computed Tomography, and Magnetoencepgalography to gain further insight into the physiology and clinical significance of periodic discharges.

New-onset refractory status epilepticus: Etiology, clinical features, and outcome.

Objectives: The aims of this study were to determine the etiology, clinical features, and predictors of outcome of new-onset refractory status epilepticus.

Methods: Retrospective review of patients with refractory status epilepticus without etiology identified within 48 hours of admission between January 1, 2008, and December 31, 2013, in 13 academic medical centers. The primary outcome measure was poor functional outcome at discharge (defined as a score >3 on the modified Rankin Scale).