Edilberto Amorim

ORCID: 0000-0001-6972-5622
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About
Contact & Profiles
Research Areas
  • Cardiac Arrest and Resuscitation
  • Traumatic Brain Injury and Neurovascular Disturbances
  • EEG and Brain-Computer Interfaces
  • Traumatic Brain Injury Research
  • Epilepsy research and treatment
  • Acute Ischemic Stroke Management
  • Non-Invasive Vital Sign Monitoring
  • Heart Rate Variability and Autonomic Control
  • Neonatal and fetal brain pathology
  • Stroke Rehabilitation and Recovery
  • Intensive Care Unit Cognitive Disorders
  • Functional Brain Connectivity Studies
  • Sepsis Diagnosis and Treatment
  • Family and Patient Care in Intensive Care Units
  • ECG Monitoring and Analysis
  • Trauma and Emergency Care Studies
  • Healthcare professionals’ stress and burnout
  • Electroconvulsive Therapy Studies
  • Cerebrovascular and Carotid Artery Diseases
  • Artificial Intelligence in Healthcare and Education
  • Venous Thromboembolism Diagnosis and Management
  • Intracranial Aneurysms: Treatment and Complications
  • Medical Imaging and Analysis
  • Advanced MRI Techniques and Applications
  • Neurosurgical Procedures and Complications

University of California, San Francisco
2019-2025

Neurology, Inc
2025

Institute of Materials, Minerals and Mining
2024

Massachusetts Institute of Technology
2018-2024

Massachusetts General Hospital
2015-2023

San Francisco General Hospital
2019-2023

Michigan State University
2023

Shanghai Jiao Tong University
2023

Beth Israel Deaconess Medical Center
2022-2023

San Francisco VA Medical Center
2023

The critical care management of patients after cardiac arrest is burdened by a lack high-quality clinical studies and the resultant high-certainty evidence. This results in limited practice guideline recommendations, which may lead to uncertainty variability management. Critical crucial affects outcome. Although guidelines address some relevant topics (including temperature control neurological prognostication comatose survivors, 2 for there are more robust studies), many important subject...

10.1007/s12028-023-01871-6 article EN cc-by-nc-nd Neurocritical Care 2023-12-01

<h3>Background and Objectives</h3> Disorders of consciousness, EEG background suppression, epileptic seizures are associated with poor outcome after cardiac arrest. Our objective was to identify the distribution diffusion MRI–measured anoxic brain injury arrest define regional correlates disorders seizures. <h3>Methods</h3> We analyzed patients from a single-center database unresponsive who underwent MRI (n = 204). classified each patient according recovery consciousness (command following)...

10.1212/wnl.0000000000013301 article EN Neurology 2022-01-11

Biomedical research and clinical practice are in the midst of a transition toward significantly increased use artificial intelligence (AI) machine learning (ML) methods. These advances promise to enable qualitatively deeper insight into complex challenges formerly beyond reach analytic methods human intuition while placing demands on ethical explainable (XAI), given opaque nature many deep The U.S. National Institutes Health (NIH) has initiated significant development program, Bridge2AI,...

10.1101/2024.10.23.619844 preprint EN cc-by-nd bioRxiv (Cold Spring Harbor Laboratory) 2024-10-25

Amphetamines possess sympathomimetic properties that can affect cerebral vasculature though conflicting reports exist about their effect on vasospasm risk and clinical outcomes in aneurysmal subarachnoid hemorrhage. This study aimed to characterize the impact of recent amphetamine use development hemorrhage as well neurological outcomes. We retrospectively screened 441 consecutive patients admitted with a diagnosis who underwent at least one digital subtraction angiogram. Patients were...

10.3389/fneur.2024.1480401 article EN cc-by Frontiers in Neurology 2025-01-07

Electroencephalography (EEG) remains underutilized for stroke characterization. We sought to assess the performance of EEG Correlate Of Injury Nervous system (COIN) index, a quantitative metric designed recognition in children, discriminating large from small ischemic strokes adults. Retrospective, single-center cohort adults with acute (within 7 days) who underwent at least 8 hours continuous monitoring hospital. Stroke size was categorized as or based on threshold 100 mL using ABC/2...

10.1097/wnp.0000000000001151 article EN Journal of Clinical Neurophysiology 2025-03-05

Objectives: To review the timing of extracorporeal life support (ECLS)-related focal cerebral injury (FCI) in relation to circuit interruptions children and young adults. Design: Retrospective study from January 1, 2015, December 31, 2023. Setting: Single-center academic children’s hospital. Patients: Children adults younger than 21 years old who had neuroimaging during or after ECLS. Multiple ECLS runs individual patients were analyzed as distinct runs. Interventions: None. Measurements...

10.1097/pcc.0000000000003736 article EN Pediatric Critical Care Medicine 2025-04-01

Continuous EEG screening using spectrograms or compressed spectral arrays (CSAs) by neurophysiologists has shorter review times with minimal loss of sensitivity for seizure detection when compared visual analysis raw EEG. Limited data are available on the performance characteristics CSA-based neurocritical care nurses.This is a prospective cross-sectional study that was conducted in two academic units and involved 33 neurointensive unit nurses four neurophysiologists.All underwent brief...

10.1097/wnp.0000000000000368 article EN Journal of Clinical Neurophysiology 2016-12-08

Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram change over time. We explore whether time dependence exists for an expanded selection quantitative and accounting this enables better predictions.Retrospective.ICUs at four academic medical centers in the United States.Comatose patients with acute hypoxic-ischemic encephalopathy.None.We analyzed 12,397 hours from 438 subjects....

10.1097/ccm.0000000000003840 article EN Critical Care Medicine 2019-06-28

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

10.1371/journal.pone.0265254 article EN cc-by PLoS ONE 2022-04-07

OBJECTIVES: To address areas in which there is no consensus for the technologies, effort, and training necessary to integrate interpret information from multimodality neuromonitoring (MNM). DESIGN: A three-round Delphi process. SETTING: Electronic surveys virtual meeting. SUBJECTS: Participants with broad MNM expertise adult pediatric intensive care backgrounds. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Two rounds of were completed followed by a meeting resolve without final survey...

10.1097/ccm.0000000000006016 article EN Critical Care Medicine 2023-08-21

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 retrospective. SETTING: Seven academic or teaching hospitals from United States Europe. PATIENTS: Individuals 16 years old older who were comatose after return of spontaneous circulation following arrest had...

10.1097/ccm.0000000000006074 article EN Critical Care Medicine 2023-10-19

Abstract 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 their small RNA cargoes following stroke is unknown. We sought to characterize L1EV RNAs assess signatures for diagnosing using weighted gene co-expression network analysis random forest (RF) machine learning algorithms. Interestingly, sequencing plasma from patients with control (n =...

10.1038/s41598-024-63633-4 article EN cc-by Scientific Reports 2024-06-12
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