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Yayıcı Köken Ö, Şekeroğlu B, Şanlıdağ B, Sarı Yanartaş M, Yılmaz A. Focality in childhood absence epilepsy. Neurol Res 2024; 46:626-633. [PMID: 38643974 DOI: 10.1080/01616412.2024.2339114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 03/31/2024] [Indexed: 04/23/2024]
Abstract
BACKGROUND AND PURPOSE Childhood absence epilepsy (CAE) has a typical electroencephalography (EEG) pattern of generalized 3 Hz spike and wave discharges (SWD). Focal interictal discharges were also documented in a small number of documents. The aim was to investigate the amplitudes of interictal 3 Hz SWD within the 1st second in drug-naïve CAE patients. In this way, areas with maximal electronegativity at the beginning of clinically generalized discharges will be documented. METHODS The EEG records of children with drug-naïve CAE were evaluated retrospectively by two child neurologists first for 3 Hz SWD. Then, a machine-learning model evaluated the amplitudes of 3 Hz in the 1st second of SWD. Maximum electronegativity areas were documented and classified as focal, bilateral, and generalized. RESULTS One hundred and twelve 3 Hz SWD were evaluated in 11 patients. Among discharges within the 1st second, maximum electronegativity areas were documented as focal for 44 (39.2%), bilateral for 8 (7.1%), generalized for 60 (53.5%). Among focal electronegativity areas, mostly right central, left occipital and midline parietal areas were documented in 12 (10.7%), 7 (6.2%), and 6 (5.3%), respectively. Eight (7.1%) of the maximum electronegativity areas were detected bilaterally, of which 7 (6.2%) originated from the frontopolar areas. CONCLUSIONS Focal maximal electronegativity areas were frequently observed in drug-naïve CAE patients, comprising approximately half of non-generalized discharges. Focal discharges might be misleading in diagnosis. Focal areas within the brain may be responsible for and contribute to absence seizures that appear bilaterally symmetrical and generalized clinically. Although its clinical implication is unknown, this warrants further study.
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Affiliation(s)
- Özlem Yayıcı Köken
- Faculty of Medicine, Department of Pediatric Neurology, Akdeniz University, Antalya, Turkey
| | - Boran Şekeroğlu
- Artificial Intelligence Engineering, Near East University, Nicosia, Cyprus
- DESAM Institute, Near East University, Nicosia, Cyprus
| | - Burçin Şanlıdağ
- Faculty of Medicine, Department of Pediatric Neurology, Near East University, Nicosia, Cyprus
| | - Mehpare Sarı Yanartaş
- Faculty of Medicine, Department of Pediatric Neurology, Akdeniz University, Antalya, Turkey
| | - Arzu Yılmaz
- Ministry of Health, Ankara Research and Training Hospital, Department of Pediatric Neurology, Ankara, Turkey
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2
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Vitt JR, Mainali S. Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients. Semin Neurol 2024; 44:342-356. [PMID: 38569520 DOI: 10.1055/s-0044-1785504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
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.
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Affiliation(s)
- Jeffrey R Vitt
- Department of Neurological Surgery, UC Davis Medical Center, Sacramento, California
| | - Shraddha Mainali
- Department of Neurology, Virginia Commonwealth University, Richmond, Virginia
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3
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Glimmerveen AB, Verhulst MMLH, de Kruijf NLM, van Gils P, Delnoij T, Bonnes J, van Heugten CM, Van Putten MJAM, Hofmeijer J. Resting state EEG relates to short- and long-term cognitive functioning after cardiac arrest. Resuscitation 2024; 201:110253. [PMID: 38797387 DOI: 10.1016/j.resuscitation.2024.110253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/17/2024] [Accepted: 05/18/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Approximately half of cardiac arrest survivors have persistent cognitive impairment. Guidelines recommend early screening to identify patients at risk for cognitive impairment, but there is no consensus on the best screening method. We aimed to identify quantitative EEG measures relating with short- and long-term cognitive function after cardiac arrest for potential to cognitive outcome prediction. METHODS We analyzed data from a prospective longitudinal multicenter cohort study designed to develop a prediction model for cognitive outcome after cardiac arrest. For the current analysis, we used twenty-minute EEG registrations from 80 patients around one week after cardiac arrest. We calculated power spectral density, normalized alpha-to-theta ratio (nATR), peak frequency, and center of gravity (CoG) of this peak frequency. We related these with global cognitive functioning (scores on the Montreal Cognitive Assessment (MoCA)) at one week, three and twelve months follow-up with multivariate mixed effect models, and with performance on standard neuropsychological examination at twelve months using Pearson correlation coefficients. RESULTS Each individual EEG parameter related to MoCA at one week (βnATR = 7.36; P < 0.01; βpeak frequency = 1.73, P < 0.01; βCoG = -9.88, P < 0.01). The nATR also related with the MoCA at three months ((βnATR = 2.49; P 0.01). No EEG metrics significantly related to the MoCA score at twelve months. nATR and peak frequency related with memory performance at twelve months. Results were consistent in sensitivity analyses. CONCLUSION Early resting-state EEG parameters relate with short-term global cognitive functioning and with memory function at one year after cardiac arrest. Additional predictive values in multimodal prediction models need further study.
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Affiliation(s)
- A B Glimmerveen
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
| | - M M L H Verhulst
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - N L M de Kruijf
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - P van Gils
- Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Maastrich University, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht, The Netherlands; Maastricht University, Limburg Brain Injury Center, Maastricht, The Netherlands
| | - T Delnoij
- Department of Intensive Care Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - J Bonnes
- Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - C M van Heugten
- Maastricht University, Limburg Brain Injury Center, Maastricht, The Netherlands; Maastricht University, Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht, The Netherlands
| | - M J A M Van Putten
- Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, The Netherlands
| | - J Hofmeijer
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
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4
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Chen CC, Massey SL, Kirschen MP, Yuan I, Padiyath A, Simpao AF, Tsui FR. Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review. Resuscitation 2024; 194:110049. [PMID: 37972682 PMCID: PMC11023717 DOI: 10.1016/j.resuscitation.2023.110049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023]
Abstract
AIM OF THE REVIEW The primary aim of this systematic review was to investigate the most common electroencephalogram (EEG)-based machine learning (ML) model with the highest Area Under Receiver Operating Characteristic Curve (AUC) in two ML categories, conventional ML and Deep Neural Network (DNN), to predict the neurologic outcomes after cardiac arrest; the secondary aim was to investigate common EEG features applied to ML models. METHODS Systematic search of medical literature from PubMed and engineering literature from Compendex up to June 2, 2023. One reviewer screened studies that used EEG-based ML models to predict the neurologic outcomes after cardiac arrest. Four reviewers validated that the studies met selection criteria. Nine variables were manually extracted. The top-five common EEG features were calculated. We evaluated each study's risk of bias using the Quality in Prognosis Studies guideline. RESULTS Out of 351 identified studies, 17 studies met the inclusion criteria. Random Forest (RF) (n = 7) was the most common ML model in the conventional ML category (n = 11), followed by Convolutional Neural Network (CNN) (n = 4) in the DNN category (n = 6). The AUCs for RF ranged between 0.8 and 0.97, while CNN had AUCs between 0.7 and 0.92. The top-three commonly used EEG features were band power (n = 12), Shannon's Entropy (n = 11), burst-suppression ratio (n = 9). CONCLUSIONS RF and CNN were the two most common ML models with the highest AUCs for predicting the neurologic outcomes after cardiac arrest. Using a multimodal model that combines EEG features and electronic health record data may further improve prognostic performance.
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Affiliation(s)
- Chao-Chen Chen
- Tsui Laboratory, Children's Hospital of Philadelphia, 734 Schuylkill Ave, Philadelphia, PA 19146, United States; Department of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210 S 33rd St, Philadelphia, PA 19104, United States
| | - Shavonne L Massey
- Department of Neurology and Pediatrics, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Matthew P Kirschen
- Department of Neurology and Pediatrics, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Ian Yuan
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Asif Padiyath
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Allan F Simpao
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Fuchiang Rich Tsui
- Tsui Laboratory, Children's Hospital of Philadelphia, 734 Schuylkill Ave, Philadelphia, PA 19146, United States; Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, United States.
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5
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Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, Lee JW, Herman ST, Sivaraju A, Gaspard N, Hofmeijer J, van Putten MJAM, Sameni R, Reyna MA, Clifford GD, Westover MB. The International Cardiac Arrest Research Consortium Electroencephalography Database. Crit Care Med 2023; 51:1802-1811. [PMID: 37855659 PMCID: PMC10841086 DOI: 10.1097/ccm.0000000000006074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
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. PATIENTS Individuals 16 years old or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous electroencephalography monitoring were included. INTERVENTIONS Not applicable. MEASUREMENTS AND MAIN RESULTS Clinical and electroencephalography data were harmonized and stored in a common Waveform Database-compatible format. Automated spike frequency, background continuity, and artifact detection on electroencephalography were calculated with 10-second resolution and summarized hourly. Neurologic outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical data and 56,676 hours (3.9 terabytes) of continuous electroencephalography data for 1,020 patients. Most patients died ( n = 603, 59%), 48 (5%) had severe neurologic disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean electroencephalography recording duration depending on the neurologic outcome (range, 53-102 hr for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least 1 hour was seen in 258 patients (25%) (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least 1 hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. CONCLUSIONS The I-CARE consortium electroencephalography database provides a comprehensive real-world clinical and electroencephalography dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal electroencephalography patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.
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Affiliation(s)
- Edilberto Amorim
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wei-Long Zheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, CN
| | - Mohammad M. Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Mahsa Aghaeeaval
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Pravinkumar Kandhare
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Vishnu Karukonda
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Susan T. Herman
- Department of Neurology, Barrow Neurological Institute, Comprehensive Epilepsy Center, Phoenix, Arizona, USA
| | - Adithya Sivaraju
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nicolas Gaspard
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neurology, Universite Libre de Bruxelles, Brussels, Belgium
| | - Jeannette Hofmeijer
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Michel J. A. M. van Putten
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, The Netherlands
| | - Reza Sameni
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Matthew A. Reyna
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
- Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, Georgia, USA
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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Kenda M, Leithner C. On the path to artificial intelligence analysis of brain CT after cardiac arrest. Resuscitation 2023; 191:109947. [PMID: 37634861 DOI: 10.1016/j.resuscitation.2023.109947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 08/11/2023] [Indexed: 08/29/2023]
Affiliation(s)
- Martin Kenda
- Charité - Universitätsmedizin Berlin, Corporate member of Freie Universität and Humboldt-Universität zu Berlin, Department of Neurology, Augustenburger Platz 1, 13353 Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Junior Digital Clinician Scientist Program, Charitéplatz 1, 10117 Berlin, Germany
| | - Christoph Leithner
- Charité - Universitätsmedizin Berlin, Corporate member of Freie Universität and Humboldt-Universität zu Berlin, Department of Neurology, Augustenburger Platz 1, 13353 Berlin, Germany.
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7
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Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, Lee JW, Herman ST, Sivaraju A, Gaspard N, Hofmeijer J, van Putten MJAM, Sameni R, Reyna MA, Clifford GD, Westover MB. The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.28.23294672. [PMID: 37693458 PMCID: PMC10491275 DOI: 10.1101/2023.08.28.23294672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
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.S. and Europe. Patients Individuals aged 16 or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous EEG monitoring were included. Interventions not applicable. Measurements and Main Results Clinical and EEG data were harmonized and stored in a common Waveform Database (WFDB)-compatible format. Automated spike frequency, background continuity, and artifact detection on EEG were calculated with 10 second resolution and summarized hourly. Neurological outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical and 56,676 hours (3.9 TB) of continuous EEG data for 1,020 patients. Most patients died (N=603, 59%), 48 (5%) had severe neurological disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean EEG recording duration depending on the neurological outcome (range 53-102h for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least one hour was seen in 258 (25%) patients (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least one hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. Conclusions The International Cardiac Arrest Research (I-CARE) consortium database provides a comprehensive real-world clinical and EEG dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal EEG patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.
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Affiliation(s)
- Edilberto Amorim
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wei-Long Zheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, CN
| | - Mohammad M. Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Mahsa Aghaeeaval
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Pravinkumar Kandhare
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Vishnu Karukonda
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Susan T. Herman
- Department of Neurology, Barrow Neurological Institute, Comprehensive Epilepsy Center, Phoenix, Arizona, USA
| | - Adithya Sivaraju
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nicolas Gaspard
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neurology, Universite Libre de Bruxelles, Brussels, Belgium
| | - Jeannette Hofmeijer
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Michel J. A. M. van Putten
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, The Netherlands
| | - Reza Sameni
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Matthew A. Reyna
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
- Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, Georgia, USA
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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8
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Zubler F, Tzovara A. Deep learning for EEG-based prognostication after cardiac arrest: from current research to future clinical applications. Front Neurol 2023; 14:1183810. [PMID: 37560450 PMCID: PMC10408678 DOI: 10.3389/fneur.2023.1183810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 07/03/2023] [Indexed: 08/11/2023] Open
Abstract
Outcome prognostication in comatose patients after cardiac arrest (CA) remains to date a challenge. The major determinant of clinical outcome is the post-hypoxic/ischemic encephalopathy. Electroencephalography (EEG) is routinely used to assess neural functions in comatose patients. Currently, EEG-based outcome prognosis relies on visual evaluation by medical experts, which is time consuming, prone to subjectivity, and oblivious to complex patterns. The field of deep learning has given rise to powerful algorithms for detecting patterns in large amounts of data. Analyzing EEG signals of coma patients with deep neural networks with the goal of assisting in outcome prognosis is therefore a natural application of these algorithms. Here, we provide the first narrative literature review on the use of deep learning for prognostication after CA. Existing studies show overall high performance in predicting outcome, relying either on spontaneous or on auditory evoked EEG signals. Moreover, the literature is concerned with algorithmic interpretability, and has shown that largely, deep neural networks base their decisions on clinically or neurophysiologically meaningful features. We conclude this review by discussing considerations that the fields of artificial intelligence and neurology will need to jointly address in the future, in order for deep learning algorithms to break the publication barrier, and to be integrated in clinical practice.
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Affiliation(s)
- Frederic Zubler
- Department of Neurology, Spitalzentrum Biel, University of Bern, Biel/Bienne, Switzerland
| | - Athina Tzovara
- Institute of Computer Science, University of Bern, Bern, Switzerland
- Department of Neurology, Zentrum für Experimentelle Neurologie and Sleep Wake Epilepsy Center—Neurotec, Inselspital University Hospital Bern, Bern, Switzerland
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9
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Rajajee V, Muehlschlegel S, Wartenberg KE, Alexander SA, Busl KM, Chou SHY, Creutzfeldt CJ, Fontaine GV, Fried H, Hocker SE, Hwang DY, Kim KS, Madzar D, Mahanes D, Mainali S, Meixensberger J, Montellano F, Sakowitz OW, Weimar C, Westermaier T, Varelas PN. Guidelines for Neuroprognostication in Comatose Adult Survivors of Cardiac Arrest. Neurocrit Care 2023; 38:533-563. [PMID: 36949360 PMCID: PMC10241762 DOI: 10.1007/s12028-023-01688-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 01/30/2023] [Indexed: 03/24/2023]
Abstract
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.
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Affiliation(s)
- Venkatakrishna Rajajee
- Departments of Neurology and Neurosurgery, 3552 Taubman Health Care Center, SPC 5338, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5338, USA.
| | - Susanne Muehlschlegel
- Departments of Neurology, Anesthesiology, and Surgery, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | | | - Katharina M Busl
- Departments of Neurology and Neurosurgery, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Sherry H Y Chou
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Gabriel V Fontaine
- Departments of Pharmacy and Neurosciences, Intermountain Healthcare, Salt Lake City, UT, USA
| | - Herbert Fried
- Department of Neurosurgery, Denver Health Medical Center, Denver, CO, USA
| | - Sara E Hocker
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - David Y Hwang
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Keri S Kim
- Pharmacy Practice, University of Illinois, Chicago, IL, USA
| | - Dominik Madzar
- Department of Neurology, University of Erlangen, Erlangen, Germany
| | - Dea Mahanes
- Departments of Neurology and Neurosurgery, University of Virginia Health, Charlottesville, VA, USA
| | - Shraddha Mainali
- Department of Neurology, Virginia Commonwealth University, Richmond, VA, USA
| | | | | | - Oliver W Sakowitz
- Department of Neurosurgery, Neurosurgery Center Ludwigsburg-Heilbronn, Ludwigsburg, Germany
| | - Christian Weimar
- Institute of Medical Informatics, Biometry, and Epidemiology, University Hospital Essen, Essen, Germany
- BDH-Clinic Elzach, Elzach, Germany
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Elmer J, Kurz MC, Coppler PJ, Steinberg A, DeMasi S, De-Arteaga M, Simon N, Zadorozny VI, Flickinger KL, Callaway CW. Time to Awakening and Self-Fulfilling Prophecies After Cardiac Arrest. Crit Care Med 2023; 51:503-512. [PMID: 36752628 PMCID: PMC10023349 DOI: 10.1097/ccm.0000000000005790] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
OBJECTIVES Withdrawal of life-sustaining therapies for perceived poor neurologic prognosis (WLST-N) is common after resuscitation from cardiac arrest and may bias outcome estimates from models trained using observational data. We compared several approaches to outcome prediction with the goal of identifying strategies to quantify and reduce this bias. DESIGN Retrospective observational cohort study. SETTING Two academic medical centers ("UPMC" and "University of Alabama Birmingham" [UAB]). PATIENTS Comatose adults resuscitated from cardiac arrest. INTERVENTION None. MEASUREMENTS AND MAIN RESULTS As potential predictors, we considered clinical, laboratory, imaging, and quantitative electroencephalography data available early after hospital arrival. We followed patients until death, discharge, or awakening from coma. We used penalized Cox regression with a least absolute shrinkage and selection operator penalty and five-fold cross-validation to predict time to awakening in UPMC patients and then externally validated the model in UAB patients. This model censored patients after WLST-N, considering subsequent potential for awakening to be unknown. Next, we developed a penalized logistic model predicting awakening, which treated failure to awaken after WLST-N as a true observed outcome, and a separate logistic model predicting WLST-N. We scaled and centered individual patients' Cox and logistic predictions for awakening to allow direct comparison and then explored the difference in predictions across probabilities of WLST-N. Overall, 1,254 patients were included, and 29% awakened. Cox models performed well (mean area under the curve was 0.93 in the UPMC test sets and 0.83 in external validation). Logistic predictions of awakening were systematically more pessimistic than Cox-based predictions for patients at higher risk of WLST-N, suggesting potential for self-fulfilling prophecies to arise when failure to awaken after WLST-N is considered as the ground truth outcome. CONCLUSIONS Compared with traditional binary outcome prediction, censoring outcomes after WLST-N may reduce potential for bias and self-fulfilling prophecies.
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Affiliation(s)
- Jonathan Elmer
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
| | - Michael C. Kurz
- Department of Emergency Medicine, University of Alabama-Birmingham Birmingham Alabama USA
- Department of Surgery, Division of Acute Care Surgery, University of Alabama-Birmingham Birmingham Alabama USA
- Center for Injury Science, University of Alabama-Birmingham Birmingham Alabama USA
| | - Patrick J Coppler
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
| | - Alexis Steinberg
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
| | - Stephanie DeMasi
- Department of Emergency Medicine, Virginia Comonwealth University, Richmond, Virginia, USA
| | - Maria De-Arteaga
- Information, Risk and Operations Management Department, McCombs School of Business, University of Texas at Austin, Austin, TX USA
| | - Noah Simon
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA USA
| | | | - Katharyn L. Flickinger
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
| | - Clifton W Callaway
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
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11
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Viderman D, Abdildin YG, Batkuldinova K, Badenes R, Bilotta F. Artificial Intelligence in Resuscitation: A Scoping Review. J Clin Med 2023; 12:jcm12062254. [PMID: 36983255 PMCID: PMC10054374 DOI: 10.3390/jcm12062254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/17/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Introduction: Cardiac arrest is a significant cause of premature mortality and severe disability. Despite the death rate steadily decreasing over the previous decade, only 22% of survivors achieve good clinical status and only 25% of patients survive until their discharge from the hospital. The objective of this scoping review was to review relevant AI modalities and the main potential applications of AI in resuscitation. Methods: We conducted the literature search for related studies in PubMed, EMBASE, and Google Scholar. We included peer-reviewed publications and articles in the press, pooling and characterizing the data by their model types, goals, and benefits. Results: After identifying 268 original studies, we chose 59 original studies (reporting 1,817,419 patients) to include in the qualitative synthesis. AI-based methods appear to be superior to traditional methods in achieving high-level performance. Conclusion: AI might be useful in predicting cardiac arrest, heart rhythm disorders, and post-cardiac arrest outcomes, as well as in the delivery of drone-delivered defibrillators and notification of dispatchers. AI-powered technologies could be valuable assistants to continuously track patient conditions. Healthcare professionals should assist in the research and development of AI-powered technologies as well as their implementation into clinical practice.
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Affiliation(s)
- Dmitriy Viderman
- Department of Surgery, Nazarbayev University School of Medicine (NUSOM), Kerei, Zhanibek khandar Str. 5/1, Astana 010000, Kazakhstan;
| | - Yerkin G. Abdildin
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, 53 Kabanbay Batyr Ave., Astana 010000, Kazakhstan
| | - Kamila Batkuldinova
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, 53 Kabanbay Batyr Ave., Astana 010000, Kazakhstan
| | - Rafael Badenes
- Department of Anaesthesiology and Intensive Care, Hospital Clìnico Universitario de Valencia, University of Valencia, 46001 Valencia, Spain
- Correspondence:
| | - Federico Bilotta
- Department of Anesthesia and Intensive Care, University La Sapienza, 00185 Rome, Italy
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12
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Aellen FM, Alnes SL, Loosli F, Rossetti AO, Zubler F, De Lucia M, Tzovara A. Auditory stimulation and deep learning predict awakening from coma after cardiac arrest. Brain 2023; 146:778-788. [PMID: 36637902 PMCID: PMC9924902 DOI: 10.1093/brain/awac340] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/28/2022] [Accepted: 09/02/2022] [Indexed: 01/14/2023] Open
Abstract
Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considerable number of patients in a 'grey zone', with uncertain prognosis. Quantitative analysis of EEG responses to auditory stimuli can provide a window into neural functions in coma and information about patients' chances of awakening. However, responses to standardized auditory stimulation are far from being used in a clinical routine due to heterogeneous and cumbersome protocols. Here, we hypothesize that convolutional neural networks can assist in extracting interpretable patterns of EEG responses to auditory stimuli during the first day of coma that are predictive of patients' chances of awakening and survival at 3 months. We used convolutional neural networks (CNNs) to model single-trial EEG responses to auditory stimuli in the first day of coma, under standardized sedation and targeted temperature management, in a multicentre and multiprotocol patient cohort and predict outcome at 3 months. The use of CNNs resulted in a positive predictive power for predicting awakening of 0.83 ± 0.04 and 0.81 ± 0.06 and an area under the curve in predicting outcome of 0.69 ± 0.05 and 0.70 ± 0.05, for patients undergoing therapeutic hypothermia and normothermia, respectively. These results also persisted in a subset of patients that were in a clinical 'grey zone'. The network's confidence in predicting outcome was based on interpretable features: it strongly correlated to the neural synchrony and complexity of EEG responses and was modulated by independent clinical evaluations, such as the EEG reactivity, background burst-suppression or motor responses. Our results highlight the strong potential of interpretable deep learning algorithms in combination with auditory stimulation to improve prognostication of coma outcome.
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Affiliation(s)
- Florence M Aellen
- Correspondence to: Florence Aellen University of Bern; Institute for Computer Science Neubrückstrasse 10; CH-3012 Bern E-mail:
| | - Sigurd L Alnes
- Institute of Computer Science, University of Bern, Bern, Switzerland,Zentrum für Experimentelle Neurologie, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Fabian Loosli
- Institute of Computer Science, University of Bern, Bern, Switzerland
| | - Andrea O Rossetti
- Neurology Service, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Frédéric Zubler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Marzia De Lucia
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Athina Tzovara
- Correspondence may also be addressed to: Athina Tzovara E-mail:
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13
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Mainali S, Park S. Artificial Intelligence and Big Data Science in Neurocritical Care. Crit Care Clin 2023; 39:235-242. [DOI: 10.1016/j.ccc.2022.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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14
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Mertens M, King OC, van Putten MJAM, Boenink M. Can we learn from hidden mistakes? Self-fulfilling prophecy and responsible neuroprognostic innovation. JOURNAL OF MEDICAL ETHICS 2022; 48:922-928. [PMID: 34253620 PMCID: PMC9626909 DOI: 10.1136/medethics-2020-106636] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 05/22/2021] [Indexed: 05/24/2023]
Abstract
A self-fulfilling prophecy (SFP) in neuroprognostication occurs when a patient in coma is predicted to have a poor outcome, and life-sustaining treatment is withdrawn on the basis of that prediction, thus directly bringing about a poor outcome (viz. death) for that patient. In contrast to the predominant emphasis in the bioethics literature, we look beyond the moral issues raised by the possibility that an erroneous prediction might lead to the death of a patient who otherwise would have lived. Instead, we focus on the problematic epistemic consequences of neuroprognostic SFPs in settings where research and practice intersect. When this sort of SFP occurs, the problem is that physicians and researchers are never in a position to notice whether their original prognosis was correct or incorrect, since the patient dies anyway. Thus, SFPs keep us from discerning false positives from true positives, inhibiting proper assessment of novel prognostic tests. This epistemic problem of SFPs thus impedes learning, but ethical obligations of patient care make it difficult to avoid SFPs. We then show how the impediment to catching false positive indicators of poor outcome distorts research on novel techniques for neuroprognostication, allowing biases to persist in prognostic tests. We finally highlight a particular risk that a precautionary bias towards early withdrawal of life-sustaining treatment may be amplified. We conclude with guidelines about how researchers can mitigate the epistemic problems of SFPs, to achieve more responsible innovation of neuroprognostication for patients in coma.
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Affiliation(s)
- Mayli Mertens
- Center for Medical Science and Technology Studies, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
- Department of Philosophy, University of Twente, Enschede, Overijssel, The Netherlands
| | - Owen C King
- Department of Philosophy, University of Twente, Enschede, Overijssel, The Netherlands
| | - Michel J A M van Putten
- MIRA-Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, Overijssel, The Netherlands
- Department of Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, Overijssel, The Netherlands
| | - Marianne Boenink
- Department of Philosophy, University of Twente, Enschede, Overijssel, The Netherlands
- Department IQ Healthcare, RadboudUMC - Radboud University, Nijmegen, Gelderland, the Netherlands
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15
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Jonas S, Müller M, Rossetti AO, Rüegg S, Alvarez V, Schindler K, Zubler F. Diagnostic and prognostic EEG analysis of critically ill patients: A deep learning study. Neuroimage Clin 2022; 36:103167. [PMID: 36049354 PMCID: PMC9441331 DOI: 10.1016/j.nicl.2022.103167] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 06/16/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022]
Abstract
Visual interpretation of electroencephalography (EEG) is time consuming, may lack objectivity, and is restricted to features detectable by a human. Computer-based approaches, especially deep learning, could potentially overcome these limitations. However, most deep learning studies focus on a specific question or a single pathology. Here we explore the potential of deep learning for EEG-based diagnostic and prognostic assessment of patients with acute consciousness impairment (ACI) of various etiologies. EEGs from 358 adults from a randomized controlled trial (CERTA, NCT03129438) were retrospectively analyzed. A convolutional neural network was used to predict the clinical outcome (based either on survival or on best cerebral performance category) and to determine the etiology (four diagnostic categories). The largest probability output served as marker for the confidence of the network in its prediction ("certainty factor"); we also systematically compared the predictions with raw EEG data, and used a visualization algorithm (Grad-CAM) to highlight discriminative patterns. When all patients were considered, the area under the receiver operating characteristic curve (AUC) was 0.721 for predicting survival and 0.703 for predicting the outcome based on best CPC; for patients with certainty factor ≥ 60 % the AUCs increased to 0.776 and 0.755 respectively; and for certainty factor ≥ 75 % to 0.852 and 0.879. The accuracy for predicting the etiology was 54.5 %; the accuracy increased to 67.7 %, 70.3 % and 84.1 % for patients with certainty factor of 50 %, 60 % and 75 % respectively. Visual analysis showed that the network learnt EEG patterns typically recognized by human experts, and suggested new criteria. This work demonstrates for the first time the potential of deep learning-based EEG analysis in critically ill patients with various etiologies of ACI. Certainty factor and post-hoc correlation of input data with prediction help to better characterize the method and pave the route for future implementations in clinical routine.
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Affiliation(s)
- Stefan Jonas
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Michael Müller
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Andrea O. Rossetti
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Stephan Rüegg
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Vincent Alvarez
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland,Department of Neurology, Hôpital du Valais, Sion, Switzerland
| | - Kaspar Schindler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Frédéric Zubler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland,Corresponding author at: Sleep-Wake-Epilepsy Center, Department of Neurology, Inselspital, Bern University Hospital, Freiburgstrasse 10, 3010 Bern, Switzerland.
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16
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Ray J, Wijesekera L, Cirstea S. Machine learning and clinical neurophysiology. J Neurol 2022; 269:6678-6684. [PMID: 35907045 DOI: 10.1007/s00415-022-11283-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/05/2022] [Accepted: 07/09/2022] [Indexed: 11/29/2022]
Abstract
Clinical neurophysiology constructs a wealth of dynamic information pertaining to the integrity and function of both central and peripheral nervous systems. As with many technological fields, there has been an explosion of data in neurophysiology over recent years, and this requires considerable analysis by experts. Computational algorithms and especially advances in machine learning (ML) have the ability to assist with this task and potentially reveal hidden insights. In this update article, we will provide a brief overview where such technology is being applied in clinical neurophysiology and possible future directions.
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Affiliation(s)
- Julian Ray
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK.
| | - Lokesh Wijesekera
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK
| | - Silvia Cirstea
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK
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17
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Outcome Prediction of Postanoxic Coma: A Comparison of Automated Electroencephalography Analysis Methods. Neurocrit Care 2022; 37:248-258. [PMID: 35233717 PMCID: PMC9343315 DOI: 10.1007/s12028-022-01449-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 01/10/2022] [Indexed: 12/03/2022]
Abstract
Background To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. Methods A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as “good” (Cerebral Performance Category 1–2) or “poor” (Cerebral Performance Category 3–5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG. Results The deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44–64%) at a false positive rate (FPR) of 0% (95% CI 0–2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52–100%) at a FPR of 12% (95% CI 0–24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83–83%) at a FPR of 3% (95% CI 3–3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels. Conclusions A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest.
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18
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Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest. Crit Care Med 2022; 50:e162-e172. [PMID: 34406171 PMCID: PMC8810601 DOI: 10.1097/ccm.0000000000005286] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVES Prognostication of neurologic status among survivors of in-hospital cardiac arrests remains a challenging task for physicians. Although models such as the Cardiac Arrest Survival Post-Resuscitation In-hospital score are useful for predicting neurologic outcomes, they were developed using traditional statistical techniques. In this study, we derive and compare the performance of several machine learning models with each other and with the Cardiac Arrest Survival Post-Resuscitation In-hospital score for predicting the likelihood of favorable neurologic outcomes among survivors of resuscitation. DESIGN Analysis of the Get With The Guidelines-Resuscitation registry. SETTING Seven-hundred fifty-five hospitals participating in Get With The Guidelines-Resuscitation from January 1, 2001, to January 28, 2017. PATIENTS Adult in-hospital cardiac arrest survivors. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Of 117,674 patients in our cohort, 28,409 (24%) had a favorable neurologic outcome, as defined as survival with a Cerebral Performance Category score of less than or equal to 2 at discharge. Using patient characteristics, pre-existing conditions, prearrest interventions, and periarrest variables, we constructed logistic regression, support vector machines, random forests, gradient boosted machines, and neural network machine learning models to predict favorable neurologic outcome. Events prior to October 20, 2009, were used for model derivation, and all subsequent events were used for validation. The gradient boosted machine predicted favorable neurologic status at discharge significantly better than the Cardiac Arrest Survival Post-Resuscitation In-hospital score (C-statistic: 0.81 vs 0.73; p < 0.001) and outperformed all other machine learning models in terms of discrimination, calibration, and accuracy measures. Variables that were consistently most important for prediction across all models were duration of arrest, initial cardiac arrest rhythm, admission Cerebral Performance Category score, and age. CONCLUSIONS The gradient boosted machine algorithm was the most accurate for predicting favorable neurologic outcomes in in-hospital cardiac arrest survivors. Our results highlight the utility of machine learning for predicting neurologic outcomes in resuscitated patients.
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Big Data and Artificial Intelligence for Precision Medicine in the Neuro-ICU: Bla, Bla, Bla. Neurocrit Care 2022; 37:163-165. [PMID: 35023043 PMCID: PMC9343268 DOI: 10.1007/s12028-021-01427-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 12/16/2021] [Indexed: 11/06/2022]
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20
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Sandroni C, Cronberg T, Hofmeijer J. EEG monitoring after cardiac arrest. Intensive Care Med 2022; 48:1439-1442. [PMID: 35471582 PMCID: PMC9468095 DOI: 10.1007/s00134-022-06697-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 04/03/2022] [Indexed: 02/04/2023]
Affiliation(s)
- Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy.
- Institute of Anaesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Rome, Italy.
| | - Tobias Cronberg
- Department of Clinical Sciences Lund, Neurology, Lund University, Skane University Hospital, Lund, Sweden
| | - Jeannette Hofmeijer
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede, The Netherlands
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
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21
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Zheng WL, Amorim E, Jing J, Wu O, Ghassemi M, Lee JW, Sivaraju A, Pang T, Herman ST, Gaspard N, Ruijter BJ, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM, Westover MB. Predicting Neurological Outcome from Electroencephalogram Dynamics in Comatose Patients after Cardiac Arrest with Deep Learning. IEEE Trans Biomed Eng 2021; 69:1813-1825. [PMID: 34962860 PMCID: PMC9087641 DOI: 10.1109/tbme.2021.3139007] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. METHODS We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation. RESULTS The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). CONCLUSIONS AND SIGNIFICANCE These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.
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22
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Pettit RW, Fullem R, Cheng C, Amos CI. Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. Emerg Top Life Sci 2021; 5:ETLS20210246. [PMID: 34927670 PMCID: PMC8786279 DOI: 10.1042/etls20210246] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022]
Abstract
AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.
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Affiliation(s)
- Rowland W. Pettit
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
| | - Robert Fullem
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, U.S.A
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, U.S.A
| | - Christopher I. Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, U.S.A
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, U.S.A
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23
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Zheng WL, Amorim E, Jing J, Ge W, Hong S, Wu O, Ghassemi M, Lee JW, Sivaraju A, Pang T, Herman ST, Gaspard N, Ruijter BJ, Sun J, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM, Westover MB. Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks. Resuscitation 2021; 169:86-94. [PMID: 34699925 PMCID: PMC8692444 DOI: 10.1016/j.resuscitation.2021.10.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/19/2021] [Accepted: 10/20/2021] [Indexed: 11/19/2022]
Abstract
OBJECTIVE Electroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest coma recovery likelihood. METHODS We developed a multiscale DNN combining convolutional neural networks (CNN) and recurrent neural networks (long short-term memory [LSTM]) using EEG and demographic information (age, gender, shockable rhythm) from a multicenter cohort of 1,038 cardiac arrest patients. The CNN learns EEG feature representations while the multiscale LSTM captures short-term and long-term EEG dynamics on multiple time scales. Poor outcome is defined as a Cerebral Performance Category (CPC) score of 3-5 and good outcome as CPC score 1-2 at 3-6 months after cardiac arrest. Performance is evaluated using area under the receiver operating characteristic curve (AUC) and calibration error. RESULTS Model performance increased with EEG duration, with AUC increasing from 0.83 (95% Confidence Interval [CI] 0.79-0.87 at 12h to 0.91 (95%CI 0.88-0.93) at 66h. Sensitivity of good and poor outcome prediction was 77% and 75% at a specificity of 90%, respectively. Sensitivity of poor outcome was 50% at a specificity of 99%. Predicted probability was well matched to the observation frequency of poor outcomes, with a calibration error of 0.11 [0.09-0.14]. CONCLUSIONS These results demonstrate that incorporating EEG evolution over time improves the accuracy of neurologic outcome prediction for patients with coma after cardiac arrest.
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Affiliation(s)
- Wei-Long Zheng
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Edilberto Amorim
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Shenda Hong
- Department of Computer Science, University of Illinois at Urbana Champaign, Champaign, IL, USA
| | - Ona Wu
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mohammad Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Adithya Sivaraju
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Trudy Pang
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Nicolas Gaspard
- Department of Neurology, Université Libre de Bruxelles, Brussels, Belgium
| | - Barry J Ruijter
- Department of Clinical Neurophysiology, University of Twente, Enschede, the Netherlands
| | - Jimeng Sun
- Department of Computer Science, University of Illinois at Urbana Champaign, Champaign, IL, USA
| | - Marleen C Tjepkema-Cloostermans
- Departments of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, the Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands
| | - Jeannette Hofmeijer
- Department of Clinical Neurophysiology, University of Twente, Enschede, the Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands
| | - Michel J A M van Putten
- Department of Clinical Neurophysiology, University of Twente, Enschede, the Netherlands; Departments of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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24
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Hierarchical Cluster Analysis Identifies Distinct Physiological States After Acute Brain Injury. Neurocrit Care 2021; 36:630-639. [PMID: 34661861 DOI: 10.1007/s12028-021-01362-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 09/20/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Analysis of intracranial multimodality monitoring data is challenging, and quantitative methods may help identify unique physiological signatures that inform therapeutic strategies and outcome prediction. The aim of this study was to test the hypothesis that data-driven approaches can identify distinct physiological states from intracranial multimodality monitoring data. METHODS This was a single-center retrospective observational study of patients with either severe traumatic brain injury or high-grade subarachnoid hemorrhage who underwent invasive multimodality neuromonitoring. We used hierarchical cluster analysis to group hourly values for heart rate, mean arterial pressure, intracranial pressure, brain tissue oxygen, and cerebral microdialysis across all included patients into distinct groups. Average values for measured physiological variables were compared across the identified clusters, and physiological profiles from identified clusters were mapped onto physiological states known to occur after acute brain injury. The distribution of clusters was compared between patients with favorable outcome (discharged to home or acute rehab) and unfavorable outcome (in-hospital death or discharged to chronic nursing facility). RESULTS A total of 1704 observations from 20 patients were included. Even though the difference in mean values for measured variables between patients with favorable and unfavorable outcome were small, we identified four distinct clusters within our data: (1) events with low brain tissue oxygen and high lactate-to-pyruvate ratio-values (consistent with cerebral ischemia), (2) events with higher intracranial pressure values without evidence for ischemia (3) events which appeared to be physiologically "normal," and (4) events with high cerebral lactate without brain hypoxia (consistent with cerebral hyperglycolysis). Patients with a favorable outcome had a greater proportion of cluster 3 (normal) events, whereas patients with an unfavorable outcome had a greater proportion of cluster 1 (ischemia) and cluster 4 (hyperglycolysis) events (p < 0.0001, Fisher-Freeman-Halton test). CONCLUSIONS A data-driven approach can identify distinct groupings from invasive multimodality neuromonitoring data that may have implications for therapeutic strategies and outcome predictions. These groupings could be used as classifiers to train machine learning models that can aid in the treatment of patients with acute brain injury. Further work is needed to replicate the findings of this exploratory study in larger data sets.
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25
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Mertens M, van Til J, Bouwers-Beens E, Boenink M. Chasing Certainty After Cardiac Arrest: Can a Technological Innovation Solve a Moral Dilemma? NEUROETHICS-NETH 2021. [DOI: 10.1007/s12152-021-09473-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
AbstractWhen information on a coma patient’s expected outcome is uncertain, a moral dilemma arises in clinical practice: if life-sustaining treatment is continued, the patient may survive with unacceptably poor neurological prospects, but if withdrawn a patient who could have recovered may die. Continuous electroencephalogram-monitoring (cEEG) is expected to substantially improve neuroprognostication for patients in coma after cardiac arrest. This raises expectations that decisions whether or not to withdraw will become easier. This paper investigates that expectation, exploring cEEG’s impacts when it becomes part of a socio-technical network in an Intensive Care Unit (ICU). Based on observations in two ICUs in the Netherlands and one in the USA that had cEEG implemented for research, we interviewed 25 family members, healthcare professionals, and surviving patients. The analysis focuses on (a) the way patient outcomes are constructed, (b) the kind of decision support these outcomes provide, and (c) how cEEG affects communication between professionals and relatives. We argue that cEEG can take away or decrease the intensity of the dilemma in some cases, while increasing uncertainty for others. It also raises new concerns. Since its actual impacts furthermore hinge on how cEEG is designed and implemented, we end with recommendations for ensuring responsible development and implementation.
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26
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Admiraal MM, Ramos LA, Delgado Olabarriaga S, Marquering HA, Horn J, van Rootselaar AF. Quantitative analysis of EEG reactivity for neurological prognostication after cardiac arrest. Clin Neurophysiol 2021; 132:2240-2247. [PMID: 34315065 DOI: 10.1016/j.clinph.2021.07.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 04/06/2021] [Accepted: 07/03/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG-R and EEG background pattern increases prognostic value for prediction of poor outcome after cardiac arrest (CA). METHODS Several types of ML models were trained with twelve quantitative features derived from EEG-R and EEG background data of 134 adult CA patients. Poor outcome was a Cerebral Performance Category score of 3-5 within 6 months. RESULTS The Random Forest (RF) trained on EEG-R showed the highest AUC of 83% (95-CI 80-86) of tested ML classifiers, predicting poor outcome with 46% sensitivity (95%-CI 40-51) and 89% specificity (95%-CI 86-92). Visual analysis of EEG-R had 80% sensitivity and 65% specificity. The RF was also the best classifier for EEG background (AUC 85%, 95%-CI 83-88) at 24 h after CA, with 62% sensitivity (95%-CI 57-67) and 84% specificity (95%-CI 79-88). Combining EEG-R and EEG background RF classifiers reduced the number of false positives. CONCLUSIONS Quantitative EEG-R using ML predicts poor outcome with higher specificity, but lower sensitivity compared to visual analysis of EEG-R, and is of some additional value to ML on EEG background data. SIGNIFICANCE Quantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data.
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Affiliation(s)
- M M Admiraal
- Amsterdam UMC, University of Amsterdam, Department of Neurology/Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, the Netherlands.
| | - L A Ramos
- Amsterdam UMC, University of Amsterdam, Department Biomedical Engineering & Physics, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, the Netherlands
| | - S Delgado Olabarriaga
- Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, the Netherlands
| | - H A Marquering
- Amsterdam UMC, University of Amsterdam, Department Biomedical Engineering & Physics, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands
| | - J Horn
- Amsterdam UMC, University of Amsterdam, Laboratory for Experimental Intensive Care and Anesthesiology, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Intensive Care, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - A F van Rootselaar
- Amsterdam UMC, University of Amsterdam, Department of Neurology/Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, the Netherlands
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27
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Vinny PW, Vishnu VY, Padma Srivastava MV. Artificial Intelligence shaping the future of neurology practice. Med J Armed Forces India 2021; 77:276-282. [PMID: 34305279 DOI: 10.1016/j.mjafi.2021.06.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 06/03/2021] [Indexed: 11/17/2022] Open
Abstract
Neurology practice has faced many challenges since Jean-Martin Charcot established its sacred tenets. Artificial Intelligence (AI) promises to revolutionize the time-tested neurology practice in unimaginable ways. AI can now diagnose stroke from CT/MRI scans, detect papilledema and diabetic retinopathy from retinal scans, interpret electroencephalogram (EEG) to prognosticate coma, detect seizure well before ictus, predict conversion of mild cognitive impairment to Alzheimer's dementia, classify neurodegenerative diseases based on gait and handwriting. Clinical practice would likely change in near future to accommodate AI as a complementary tool. The clinician should be prepared to change the perception of AI from nemesis to opportunity.
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Affiliation(s)
- P W Vinny
- Associate Professor, Department of Internal Medicine, Armed Forces Medical College, Pune, India
| | - V Y Vishnu
- Assistant Professor (Neurology), All India Institute of Medical Sciences, New Delhi, India
| | - M V Padma Srivastava
- Professor & Head (Neurology), All India Institute of Medical Sciences, New Delhi, India
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28
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Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-09986-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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29
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Artificial intelligence in critical care: Its about time! Med J Armed Forces India 2021; 77:266-275. [PMID: 34305278 DOI: 10.1016/j.mjafi.2020.10.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 10/07/2020] [Indexed: 11/21/2022] Open
Abstract
Currently, most critical care information is not expressed automatically at a granular level, rather is continually assessed by overindulged Intensive Care Unit (ICU) staff. Furthermore, due to different confounding morbidities and the uniqueness of the ICU setting, it is difficult to protocolize treatment regimens in the ICU. In highly complex ICU setting where man and resource management becomes extremely challenging, definite advancements are required to implement Artificial Intelligence (AI) for prognosticating the course of the disease to aid in informed decision-making. AI is the intelligence of a computer or computer-supervised robot to execute a piece of work commonly associated with intelligent beings, wherein the machines go beyond the realms of normal information processing by adding the characteristics of learning, sound reasoning, and weighting of the inputs. AI recognizes circuitous, relational time-series blueprint within datasets and this reasoning of analysis transcends conventional threshold-based analysis adapted in ICU protocols. AI works on the principle of a more complex form of Machine Learning by Artificial Neural Networks (ANN). These information-processing paradigms use multidimensional arrays called tensors which aid in 'learning' and 'weighting' all the information made available to it, thereby converting normal machine learning into Deep Learning. Here, the use of AI for data mining in complex ICU settings for protocol formulation and temporal representation and reasoning is discussed.
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30
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Carrasco-Gómez M, Keijzer HM, Ruijter BJ, Bruña R, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM. EEG functional connectivity contributes to outcome prediction of postanoxic coma. Clin Neurophysiol 2021; 132:1312-1320. [PMID: 33867260 DOI: 10.1016/j.clinph.2021.02.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/19/2021] [Accepted: 02/09/2021] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest. METHODS Prospective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as "good" (Cerebral Performance Category [CPC] 1-2) or "poor" (CPC 3-5). RESULTS We included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34-56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0-54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50-77%) at 100% specificity. CONCLUSION Functional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma. SIGNIFICANCE Functional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.
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Affiliation(s)
- Martín Carrasco-Gómez
- Laboratory of Cognitive and Computational Neuroscience (LNCyC), Centre for Biomedical Technology, Universidad Politécnica de Madrid, Spain; Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
| | - Hanneke M Keijzer
- Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands; Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Barry J Ruijter
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, the Netherlands
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (LNCyC), Centre for Biomedical Technology, Universidad Politécnica de Madrid, Spain; Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Marleen C Tjepkema-Cloostermans
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, the Netherlands; Neurocentrum, Medisch SpectrumTwente, Enschede, the Netherlands
| | - Jeannette Hofmeijer
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, the Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands
| | - Michel J A M van Putten
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, the Netherlands; Neurocentrum, Medisch SpectrumTwente, Enschede, the Netherlands
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31
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Khazanova D, Douglas VC, Amorim E. A matter of timing: EEG monitoring for neurological prognostication after cardiac arrest in the era of targeted temperature management. Minerva Anestesiol 2021; 87:704-713. [PMID: 33591136 DOI: 10.23736/s0375-9393.21.14793-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Neuromonitoring with electroencephalography (EEG) is an essential tool in neurological prognostication post-cardiac arrest. EEG allows reliable and real-time assessment of early changes in background patterns, development of seizures and epileptiform activity, as well as testing for background reactivity to stimuli despite use of sedation or targeted temperature management. Delayed emergence of consciousness post-cardiac arrest is common, therefore longitudinal monitoring of EEG allows the detection of trends indicative of neurological improvement before coma recovery can be observed clinically. In this review, we summarize essential recent literature in EEG monitoring for neurological prognostication post-cardiac arrest in the context of targeted temperature management, with a particular focus on the importance of the evolution of EEG patterns in the first few days following resuscitation.
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Affiliation(s)
- Darya Khazanova
- Department of Neurology, University of California, San Francisco, CA, USA.,Division of Neurology, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA
| | - Vanja C Douglas
- Department of Neurology, University of California, San Francisco, CA, USA
| | - Edilberto Amorim
- Department of Neurology, University of California, San Francisco, CA, USA - .,Division of Neurology, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA
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32
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Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data. Crit Care Explor 2021; 3:e0313. [PMID: 33458681 PMCID: PMC7803688 DOI: 10.1097/cce.0000000000000313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
To develop and characterize a machine learning algorithm to discriminate acute respiratory distress syndrome from other causes of respiratory failure using only ventilator waveform data. Design Retrospective, observational cohort study. Setting Academic medical center ICU. Patients Adults admitted to the ICU requiring invasive mechanical ventilation, including 50 patients with acute respiratory distress syndrome and 50 patients with primary indications for mechanical ventilation other than hypoxemic respiratory failure. Interventions None. Measurements and Main Results Pressure and flow time series data from mechanical ventilation during the first 24-hours after meeting acute respiratory distress syndrome criteria (or first 24-hr of mechanical ventilation for non-acute respiratory distress syndrome patients) were processed to extract nine physiologic features. A random forest machine learning algorithm was trained to discriminate between the patients with and without acute respiratory distress syndrome. Model performance was assessed using the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Analyses examined performance when the model was trained using data from the first 24 hours and tested using withheld data from either the first 24 hours (24/24 model) or 6 hours (24/6 model). Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.88, 0.90, 0.71, 0.77, and 0.90 (24/24); and 0.89, 0.90, 0.75, 0.83, and 0.83 (24/6). Conclusions Use of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation. This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings with limited availability of conventional diagnostic tests and electronic health records.
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33
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Artificial intelligence in telemetry: what clinicians should know. Intensive Care Med 2021; 47:150-153. [PMID: 33386857 PMCID: PMC7776290 DOI: 10.1007/s00134-020-06295-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 10/12/2020] [Indexed: 12/19/2022]
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34
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Gong S, Xing K, Cichocki A, Li J. Deep Learning in EEG: Advance of the Last Ten-Year Critical Period. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3079712] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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35
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Dynamic functional connectivity of the EEG in relation to outcome of postanoxic coma. Clin Neurophysiol 2021; 132:157-164. [DOI: 10.1016/j.clinph.2020.10.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 08/18/2020] [Accepted: 10/11/2020] [Indexed: 02/07/2023]
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36
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Auger SD, Jacobs BM, Dobson R, Marshall CR, Noyce AJ. Big data, machine learning and artificial intelligence: a neurologist's guide. Pract Neurol 2020; 21:practneurol-2020-002688. [PMID: 32994368 PMCID: PMC7841474 DOI: 10.1136/practneurol-2020-002688] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2020] [Indexed: 12/11/2022]
Abstract
Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.
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Affiliation(s)
- Stephen D Auger
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, UK
| | - Benjamin M Jacobs
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, UK
- Department of Neurology, Royal London Hospital, London, UK
| | - Ruth Dobson
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, UK
- Department of Neurology, Royal London Hospital, London, UK
| | - Charles R Marshall
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, UK
- Department of Neurology, Royal London Hospital, London, UK
| | - Alastair J Noyce
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, UK
- Department of Neurology, Royal London Hospital, London, UK
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Simpkins AN, Busl KM, Amorim E, Barnett-Tapia C, Cervenka MC, Dhakar MB, Etherton MR, Fung C, Griggs R, Holloway RG, Kelly AG, Khan IR, Lizarraga KJ, Madagan HG, Onweni CL, Mestre H, Rabinstein AA, Rubinos C, Dionisio-Santos DA, Youn TS, Merck LH, Maciel CB. Proceedings from the Neurotherapeutics Symposium on Neurological Emergencies: Shaping the Future of Neurocritical Care. Neurocrit Care 2020; 33:636-645. [PMID: 32959201 PMCID: PMC7736003 DOI: 10.1007/s12028-020-01085-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 08/19/2020] [Indexed: 12/11/2022]
Abstract
Effective treatment options for patients with life-threatening neurological disorders are limited. To address this unmet need, high-impact translational research is essential for the advancement and development of novel therapeutic approaches in neurocritical care. "The Neurotherapeutics Symposium 2019-Neurological Emergencies" conference, held in Rochester, New York, in June 2019, was designed to accelerate translation of neurocritical care research via transdisciplinary team science and diversity enhancement. Diversity excellence in the neuroscience workforce brings innovative and creative perspectives, and team science broadens the scientific approach by incorporating views from multiple stakeholders. Both are essential components needed to address complex scientific questions. Under represented minorities and women were involved in the organization of the conference and accounted for 30-40% of speakers, moderators, and attendees. Participants represented a diverse group of stakeholders committed to translational research. Topics discussed at the conference included acute ischemic and hemorrhagic strokes, neurogenic respiratory dysregulation, seizures and status epilepticus, brain telemetry, neuroprognostication, disorders of consciousness, and multimodal monitoring. In these proceedings, we summarize the topics covered at the conference and suggest the groundwork for future high-yield research in neurologic emergencies.
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Affiliation(s)
- Alexis N Simpkins
- Department of Neurology, McKnight Brain Institute, University of Florida College of Medicine, Room L3-100, 1149 Newell Drive, Gainesville, FL, 32611, USA.
| | - Katharina M Busl
- Department of Neurology, McKnight Brain Institute, University of Florida College of Medicine, Room L3-100, 1149 Newell Drive, Gainesville, FL, 32611, USA
- Department of Neurosurgery, University of Florida College of Medicine, Gainesville, FL, USA
| | - Edilberto Amorim
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Carolina Barnett-Tapia
- Ellen and Martin Prosserman Centre for Neuromuscular Disorders, Toronto General Hospital, Toronto, ON, Canada
| | - Mackenzie C Cervenka
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Monica B Dhakar
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Mark R Etherton
- J. Phillip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Celia Fung
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Robert Griggs
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Robert G Holloway
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Adam G Kelly
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Imad R Khan
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Karlo J Lizarraga
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Hannah G Madagan
- Department of Neurology, McKnight Brain Institute, University of Florida College of Medicine, Room L3-100, 1149 Newell Drive, Gainesville, FL, 32611, USA
| | - Chidinma L Onweni
- Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, USA
| | - Humberto Mestre
- Center for Translational Neuromedicine, Department of Neurosurgery, University of Rochester Medical Center, Rochester, USA
| | | | - Clio Rubinos
- Department of Neurology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | | | - Teddy S Youn
- Department of Neurology, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Lisa H Merck
- Department of Emergency Medicine, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Carolina B Maciel
- Department of Neurology, McKnight Brain Institute, University of Florida College of Medicine, Room L3-100, 1149 Newell Drive, Gainesville, FL, 32611, USA
- Department of Neurosurgery, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
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38
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Keijzer HM, Klop M, van Putten MJ, Hofmeijer J. Delirium after cardiac arrest: Phenotype, prediction, and outcome. Resuscitation 2020; 151:43-49. [DOI: 10.1016/j.resuscitation.2020.03.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 02/26/2020] [Accepted: 03/28/2020] [Indexed: 12/14/2022]
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39
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Zheng WL, Sun H, Akeju O, Westover MB. Adaptive Sedation Monitoring From EEG in ICU Patients With Online Learning. IEEE Trans Biomed Eng 2020; 67:1696-1706. [PMID: 31545708 PMCID: PMC7085963 DOI: 10.1109/tbme.2019.2943062] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Sedative medications are routinely administered to provide comfort and facilitate clinical care in critically ill ICU patients. Prior work shows that brain monitoring using electroencephalography (EEG) to track sedation levels may help medical personnel to optimize drug dosing and avoid the adverse effects of oversedation and undersedation. However, the performance of sedation monitoring methods proposed to date deal poorly with individual variability across patients, leading to inconsistent performance. To address this challenge we develop an online learning approach based on Adaptive Regularization of Weight Vectors (AROW). Our approach adaptively updates a sedation level prediction algorithm under a continuously evolving data distribution. The prediction model is gradually calibrated for individual patients in response to EEG observations and routine clinical assessments over time. The evaluations are performed on a population of 172 sedated ICU patients whose sedation levels were assessed using the Richmond Agitation-Sedation Scale (scores between -5 = comatose and 0 = awake). The proposed adaptive model achieves better performance than the same model without adaptation (average accuracies with tolerance of one level difference: 68.76% vs. 61.10%). Moreover, our approach is shown to be robust to sudden changes caused by label noise. Medication administrations have different effects on model performance. We find that the model performs best in patients receiving only propofol, compared to patients receiving no sedation or multiple simultaneous sedative medications.
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Machine Learning to Decode the Electroencephalography for Post Cardiac Arrest Neuroprognostication. Crit Care Med 2020; 47:1474-1476. [PMID: 31524704 DOI: 10.1097/ccm.0000000000003932] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Doerrfuss JI, Kilic T, Ahmadi M, Holtkamp M, Weber JE. Quantitative and Qualitative EEG as a Prediction Tool for Outcome and Complications in Acute Stroke Patients. Clin EEG Neurosci 2020; 51:121-129. [PMID: 31533467 DOI: 10.1177/1550059419875916] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Currently, the relevance of EEG measurements in acute stroke patients is considered low in clinical practice. However, recent studies on the predictive value of EEG measurements after stroke for various outcomes may increase the role of EEG in patients with stroke. We aimed to review the current literature on the utility of EEG measurements after stroke as a tool to predict outcome and complications, focusing on studies in which the EEG measurement was performed in the acute phase after the event and in which long-term outcome measures were reported. In our literature review, we identified 4 different outcome measures (functional outcome, mortality, development of post-stroke cognitive decline, and development of post-stroke epilepsy) where studies on the utility of acute EEG measurements exist. There is a large body of evidence for the prediction of functional outcome, in which a multitude of associated quantitative and qualitative EEG parameters are described. In contrast, only few studies focus on mortality as outcome parameter. We found studies of high methodical quality on the prediction of post-stroke cognitive decline, though the number of patients in these studies often was small. The role of EEG as a prediction tool for seizures and epilepsy after stroke could increase after a recently published study, especially if its result can be incorporated into already existing post-stroke epilepsy prediction tools. In summary, EEG is useful for the prediction of functional outcome, mortality, development of post-stroke cognitive decline and epilepsy, even though there is a discrepancy between the large amount of studies on EEG in acute stroke patients and its underuse in clinical practice.
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Affiliation(s)
- Jakob I Doerrfuss
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
| | - Tayfun Kilic
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Michael Ahmadi
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
| | - Martin Holtkamp
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Joachim E Weber
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
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