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van Putten MJAM, Ruijter BJ, Horn J, van Rootselaar AF, Tromp SC, van Kranen-Mastenbroek V, Gaspard N, Hofmeijer J. Quantitative Characterization of Rhythmic and Periodic EEG Patterns in Patients in a Coma After Cardiac Arrest and Association With Outcome. Neurology 2024; 103:e209608. [PMID: 38991197 DOI: 10.1212/wnl.0000000000209608] [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: 07/13/2024] Open
Abstract
OBJECTIVES Rhythmic and periodic patterns (RPPs) on EEG in patients in a coma after cardiac arrest are associated with a poor neurologic outcome. We characterize RPPs using qEEG in relation to outcomes. METHODS Post hoc analysis was conducted on 172 patients in a coma after cardiac arrest from the TELSTAR trial, all with RPPs. Quantitative EEG included corrected background continuity index (BCI*), relative discharge power (RDP), discharge frequency, and shape similarity. Neurologic outcomes at 3 months after arrest were categorized as poor (CPC = 3-5) or good (CPC = 1-2). RESULTS A total of 16 patients (9.3%) had a good outcome. Patients with good outcomes showed later RPP onset (28.5 vs 20.1 hours after arrest, p < 0.05) and higher background continuity at RPP onset (BCI* = 0.83 vs BCI* = 0.59, p < 0.05). BCI* <0.45 at RPP onset, maximum BCI* <0.76, RDP >0.47, or shape similarity >0.75 were consistently associated with poor outcomes, identifying 36%, 22%, 40%, or 24% of patients with poor outcomes, respectively. In patients meeting both BCI* >0.44 at RPP onset and BCI* >0.75 within 72 hours, the probability of good outcomes doubled to 18%. DISCUSSION Sufficient EEG background continuity before and during RPPs is crucial for meaningful recovery. Background continuity, discharge power, and shape similarity can help select patients with relevant chances of recovery and may guide treatment. TRIAL REGISTRATION INFORMATION February 4, 2014, ClinicalTrial.gov, NCT02056236.
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Affiliation(s)
- Michel J A M van Putten
- From the Clinical Neurophysiology Group and Department of Clinical Neurophysiology (M.J.A.M.v.P.), University of Twente and Medisch Spectrum Twente; Department of Neurology (B.J.R.), OLVG, Amsterdam; Department of Intensive Care (Janneke Horn); Department of Neurology and Clinical Neurophysiology (A.-F.v.R.), Amsterdam Neuroscience, Amsterdam UMC, the Netherlands; Department of Clinical Neurophysiology (S.C.T.), St Antonius Hospital, Nieuwegein and Department of Neurology, LUMC, Leiden; Department of Neurology (V.v.K.-M.), Maastricht UMC+, the Netherlands; Department of Neurology (N.G.), Hôpital Universitaire de Bruxelles - Hôpital Erasme, Brussels, Belgium and Department of Neurology, Yale University School of Medicine, New Haven, CT; and Department of Neurology (Jeannette Hofmeijer), Rijnstate Hospital and Clinical Neurophysiology Group, University of Twente, the Netherlands
| | - Barry J Ruijter
- From the Clinical Neurophysiology Group and Department of Clinical Neurophysiology (M.J.A.M.v.P.), University of Twente and Medisch Spectrum Twente; Department of Neurology (B.J.R.), OLVG, Amsterdam; Department of Intensive Care (Janneke Horn); Department of Neurology and Clinical Neurophysiology (A.-F.v.R.), Amsterdam Neuroscience, Amsterdam UMC, the Netherlands; Department of Clinical Neurophysiology (S.C.T.), St Antonius Hospital, Nieuwegein and Department of Neurology, LUMC, Leiden; Department of Neurology (V.v.K.-M.), Maastricht UMC+, the Netherlands; Department of Neurology (N.G.), Hôpital Universitaire de Bruxelles - Hôpital Erasme, Brussels, Belgium and Department of Neurology, Yale University School of Medicine, New Haven, CT; and Department of Neurology (Jeannette Hofmeijer), Rijnstate Hospital and Clinical Neurophysiology Group, University of Twente, the Netherlands
| | - Janneke Horn
- From the Clinical Neurophysiology Group and Department of Clinical Neurophysiology (M.J.A.M.v.P.), University of Twente and Medisch Spectrum Twente; Department of Neurology (B.J.R.), OLVG, Amsterdam; Department of Intensive Care (Janneke Horn); Department of Neurology and Clinical Neurophysiology (A.-F.v.R.), Amsterdam Neuroscience, Amsterdam UMC, the Netherlands; Department of Clinical Neurophysiology (S.C.T.), St Antonius Hospital, Nieuwegein and Department of Neurology, LUMC, Leiden; Department of Neurology (V.v.K.-M.), Maastricht UMC+, the Netherlands; Department of Neurology (N.G.), Hôpital Universitaire de Bruxelles - Hôpital Erasme, Brussels, Belgium and Department of Neurology, Yale University School of Medicine, New Haven, CT; and Department of Neurology (Jeannette Hofmeijer), Rijnstate Hospital and Clinical Neurophysiology Group, University of Twente, the Netherlands
| | - Anne-Fleur van Rootselaar
- From the Clinical Neurophysiology Group and Department of Clinical Neurophysiology (M.J.A.M.v.P.), University of Twente and Medisch Spectrum Twente; Department of Neurology (B.J.R.), OLVG, Amsterdam; Department of Intensive Care (Janneke Horn); Department of Neurology and Clinical Neurophysiology (A.-F.v.R.), Amsterdam Neuroscience, Amsterdam UMC, the Netherlands; Department of Clinical Neurophysiology (S.C.T.), St Antonius Hospital, Nieuwegein and Department of Neurology, LUMC, Leiden; Department of Neurology (V.v.K.-M.), Maastricht UMC+, the Netherlands; Department of Neurology (N.G.), Hôpital Universitaire de Bruxelles - Hôpital Erasme, Brussels, Belgium and Department of Neurology, Yale University School of Medicine, New Haven, CT; and Department of Neurology (Jeannette Hofmeijer), Rijnstate Hospital and Clinical Neurophysiology Group, University of Twente, the Netherlands
| | - Selma C Tromp
- From the Clinical Neurophysiology Group and Department of Clinical Neurophysiology (M.J.A.M.v.P.), University of Twente and Medisch Spectrum Twente; Department of Neurology (B.J.R.), OLVG, Amsterdam; Department of Intensive Care (Janneke Horn); Department of Neurology and Clinical Neurophysiology (A.-F.v.R.), Amsterdam Neuroscience, Amsterdam UMC, the Netherlands; Department of Clinical Neurophysiology (S.C.T.), St Antonius Hospital, Nieuwegein and Department of Neurology, LUMC, Leiden; Department of Neurology (V.v.K.-M.), Maastricht UMC+, the Netherlands; Department of Neurology (N.G.), Hôpital Universitaire de Bruxelles - Hôpital Erasme, Brussels, Belgium and Department of Neurology, Yale University School of Medicine, New Haven, CT; and Department of Neurology (Jeannette Hofmeijer), Rijnstate Hospital and Clinical Neurophysiology Group, University of Twente, the Netherlands
| | - Vivianne van Kranen-Mastenbroek
- From the Clinical Neurophysiology Group and Department of Clinical Neurophysiology (M.J.A.M.v.P.), University of Twente and Medisch Spectrum Twente; Department of Neurology (B.J.R.), OLVG, Amsterdam; Department of Intensive Care (Janneke Horn); Department of Neurology and Clinical Neurophysiology (A.-F.v.R.), Amsterdam Neuroscience, Amsterdam UMC, the Netherlands; Department of Clinical Neurophysiology (S.C.T.), St Antonius Hospital, Nieuwegein and Department of Neurology, LUMC, Leiden; Department of Neurology (V.v.K.-M.), Maastricht UMC+, the Netherlands; Department of Neurology (N.G.), Hôpital Universitaire de Bruxelles - Hôpital Erasme, Brussels, Belgium and Department of Neurology, Yale University School of Medicine, New Haven, CT; and Department of Neurology (Jeannette Hofmeijer), Rijnstate Hospital and Clinical Neurophysiology Group, University of Twente, the Netherlands
| | - Nicolas Gaspard
- From the Clinical Neurophysiology Group and Department of Clinical Neurophysiology (M.J.A.M.v.P.), University of Twente and Medisch Spectrum Twente; Department of Neurology (B.J.R.), OLVG, Amsterdam; Department of Intensive Care (Janneke Horn); Department of Neurology and Clinical Neurophysiology (A.-F.v.R.), Amsterdam Neuroscience, Amsterdam UMC, the Netherlands; Department of Clinical Neurophysiology (S.C.T.), St Antonius Hospital, Nieuwegein and Department of Neurology, LUMC, Leiden; Department of Neurology (V.v.K.-M.), Maastricht UMC+, the Netherlands; Department of Neurology (N.G.), Hôpital Universitaire de Bruxelles - Hôpital Erasme, Brussels, Belgium and Department of Neurology, Yale University School of Medicine, New Haven, CT; and Department of Neurology (Jeannette Hofmeijer), Rijnstate Hospital and Clinical Neurophysiology Group, University of Twente, the Netherlands
| | - Jeannette Hofmeijer
- From the Clinical Neurophysiology Group and Department of Clinical Neurophysiology (M.J.A.M.v.P.), University of Twente and Medisch Spectrum Twente; Department of Neurology (B.J.R.), OLVG, Amsterdam; Department of Intensive Care (Janneke Horn); Department of Neurology and Clinical Neurophysiology (A.-F.v.R.), Amsterdam Neuroscience, Amsterdam UMC, the Netherlands; Department of Clinical Neurophysiology (S.C.T.), St Antonius Hospital, Nieuwegein and Department of Neurology, LUMC, Leiden; Department of Neurology (V.v.K.-M.), Maastricht UMC+, the Netherlands; Department of Neurology (N.G.), Hôpital Universitaire de Bruxelles - Hôpital Erasme, Brussels, Belgium and Department of Neurology, Yale University School of Medicine, New Haven, CT; and Department of Neurology (Jeannette Hofmeijer), Rijnstate Hospital and Clinical Neurophysiology Group, University of Twente, the Netherlands
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Shivdat S, Zhan T, De Palma A, Zheng WL, Krishnamurthy P, Paneerselvam E, Snider S, Bevers M, O'Reilly UM, Lee JW, Westover MB, Amorim E. Early Burst Suppression Similarity Association with Structural Brain Injury Severity on MRI After Cardiac Arrest. Neurocrit Care 2024:10.1007/s12028-024-02047-6. [PMID: 39043984 DOI: 10.1007/s12028-024-02047-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/13/2024] [Indexed: 07/25/2024]
Abstract
BACKGROUND Identical bursts on electroencephalography (EEG) are considered a specific predictor of poor outcomes in cardiac arrest, but its relationship with structural brain injury severity on magnetic resonance imaging (MRI) is not known. METHODS This was a retrospective analysis of clinical, EEG, and MRI data from adult comatose patients after cardiac arrest. Burst similarity in first 72 h from the time of return of spontaneous circulation were calculated using dynamic time-warping (DTW) for bursts of equal (i.e., 500 ms) and varying (i.e., 100-500 ms) lengths and cross-correlation for bursts of equal lengths. Structural brain injury severity was measured using whole brain mean apparent diffusion coefficient (ADC) on MRI. Pearson's correlation coefficients were calculated between mean burst similarity across consecutive 12-24-h time blocks and mean whole brain ADC values. Good outcome was defined as Cerebral Performance Category of 1-2 (i.e., independence for activities of daily living) at the time of hospital discharge. RESULTS Of 113 patients with cardiac arrest, 45 patients had burst suppression (mean cardiac arrest to MRI time 4.3 days). Three study participants with burst suppression had a good outcome. Burst similarity calculated using DTW with bursts of varying lengths was correlated with mean ADC value in the first 36 h after cardiac arrest: Pearson's r: 0-12 h: - 0.69 (p = 0.039), 12-24 h: - 0.54 (p = 0.002), 24-36 h: - 0.41 (p = 0.049). Burst similarity measured with bursts of equal lengths was not associated with mean ADC value with cross-correlation or DTW, except for DTW at 60-72 h (- 0.96, p = 0.04). CONCLUSIONS Burst similarity on EEG after cardiac arrest may be associated with acute brain injury severity on MRI. This association was time dependent when measured using DTW.
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Affiliation(s)
- Shawn Shivdat
- Harvard College, Cambridge, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Tiange Zhan
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alessandro De Palma
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Computing, Imperial College London, London, UK
| | - Wei-Long Zheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | | | - Ezhil Paneerselvam
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Samuel Snider
- Division of Neurocritical Care, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Matthew Bevers
- Division of Neurocritical Care, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Una-May O'Reilly
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jong Woo Lee
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Division of Epilepsy, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Edilberto Amorim
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, Zuckerberg San Francisco General Hospital, 1001 Potrero Ave, Building 1, Suite 312, San Francisco, CA, 94110, USA.
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Yuan F, Damien C, Schuind S, Salvagno M, Taccone FS, Legros B, Gaspard N. Combined depth and scalp electroencephalographic monitoring in acute brain injury: Yield and prognostic value. Eur J Neurol 2024; 31:e16208. [PMID: 38270448 PMCID: PMC11235592 DOI: 10.1111/ene.16208] [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: 08/06/2023] [Revised: 10/08/2023] [Accepted: 12/28/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND AND PURPOSE Depth electroencephalography (dEEG) is an emerging neuromonitoring technology in acute brain injury (ABI). We aimed to explore the concordances between electrophysiological activities on dEEG and on scalp EEG (scEEG) in ABI patients. METHODS Consecutive ABI patients who received dEEG monitoring between 2018 and 2022 were included. Background, sporadic epileptiform discharges, rhythmic and periodic patterns (RPPs), electrographic seizures, brief potentially ictal rhythmic discharges, ictal-interictal continuum (IIC) patterns, and hourly RPP burden on dEEG and scEEG were compared. RESULTS Sixty-one ABI patients with a median dEEG monitoring duration of 114 h were included. dEEG significantly showed less continuous background (75% vs. 90%, p = 0.03), higher background amplitude (p < 0.001), more frequent rhythmic spike-and-waves (16% vs. 3%, p = 0.03), more IIC patterns (39% vs. 21%, p = 0.03), and greater hourly RPP burden (2430 vs. 1090 s/h, p = 0.01), when compared to scEEG. Among five patients with seizures on scEEG, one patient had concomitant seizures on dEEG, one had periodic discharges (not concomitant) on dEEG, and three had no RPPs on dEEG. Features and temporal occurrence of electrophysiological activities observed on dEEG and scEEG are not strongly associated. Patients with seizures and IIC patterns on dEEG seemed to have a higher rate of poor outcomes at discharge than patients without these patterns on dEEG (42% vs. 25%, p = 0.37). CONCLUSIONS dEEG can detect abnormal electrophysiological activities that may not be seen on scEEG and can be used as a complement in the neuromonitoring of ABI patients.
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Affiliation(s)
- Fang Yuan
- Neurology DepartmentSecond Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouChina
- Service de Neurologie, Hôpital Universitaire de Bruxelles, Hôpital ErasmeUniversité Libre de BruxellesBrusselsBelgium
- State Key Laboratory of Traditional Chinese Medicine SyndromeGuangzhouChina
| | - Charlotte Damien
- Service de Neurologie, Hôpital Universitaire de Bruxelles, Hôpital ErasmeUniversité Libre de BruxellesBrusselsBelgium
| | - Sophie Schuind
- Service de Neurochirurgie, Hôpital Universitaire de Bruxelles, Hôpital ErasmeUniversité Libre de BruxellesBrusselsBelgium
| | - Michele Salvagno
- Service des Soins Intensifs, Hôpital Universitaire de Bruxelles, Hôpital ErasmeUniversité Libre de BruxellesBrusselsBelgium
| | - Fabio Silvio Taccone
- Service des Soins Intensifs, Hôpital Universitaire de Bruxelles, Hôpital ErasmeUniversité Libre de BruxellesBrusselsBelgium
| | - Benjamin Legros
- Service de Neurologie, Hôpital Universitaire de Bruxelles, Hôpital ErasmeUniversité Libre de BruxellesBrusselsBelgium
| | - Nicolas Gaspard
- Service de Neurologie, Hôpital Universitaire de Bruxelles, Hôpital ErasmeUniversité Libre de BruxellesBrusselsBelgium
- Neurology DepartmentYale University School of MedicineNew HavenConnecticutUSA
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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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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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Hoedemaekers C, Hofmeijer J, Horn J. Value of EEG in outcome prediction of hypoxic-ischemic brain injury in the ICU: A narrative review. Resuscitation 2023; 189:109900. [PMID: 37419237 DOI: 10.1016/j.resuscitation.2023.109900] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/26/2023] [Accepted: 06/29/2023] [Indexed: 07/09/2023]
Abstract
Prognostication of comatose patients after cardiac arrest aims to identify patients with a large probability of favourable or unfavouble outcome, usually within the first week after the event. Electroencephalography (EEG) is a technique that is increasingly used for this purpose and has many advantages, such as its non-invasive nature and the possibility to monitor the evolution of brain function over time. At the same time, use of EEG in a critical care environment faces a number of challenges. This narrative review describes the current role and future applications of EEG for outcome prediction of comatose patients with postanoxic encephalopathy.
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Affiliation(s)
- Cornelia Hoedemaekers
- Department of Critical Care, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands.
| | - Jeannette Hofmeijer
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede, the Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands
| | - Janneke Horn
- Department of Critical Care, Amsterdam University Medical Center, Location AMC, Amsterdam, the Netherlands
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Tewarie PKB, Beernink TMJ, Eertman-Meyer CJ, Cornet AD, Beishuizen A, van Putten MJAM, Tjepkema-Cloostermans MC. Early EEG monitoring predicts clinical outcome in patients with moderate to severe traumatic brain injury. Neuroimage Clin 2023; 37:103350. [PMID: 36801601 PMCID: PMC9984683 DOI: 10.1016/j.nicl.2023.103350] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 01/23/2023] [Accepted: 02/11/2023] [Indexed: 02/16/2023]
Abstract
There is a need for reliable predictors in patients with moderate to severe traumatic brain injury to assist clinical decision making. We assess the ability of early continuous EEG monitoring at the intensive care unit (ICU) in patients with traumatic brain injury (TBI) to predict long term clinical outcome and evaluate its complementary value to current clinical standards. We performed continuous EEG measurements in patients with moderate to severe TBI during the first week of ICU admission. We assessed the Extended Glasgow Outcome Scale (GOSE) at 12 months, dichotomized into poor (GOSE 1-3) and good (GOSE 4-8) outcome. We extracted EEG spectral features, brain symmetry index, coherence, aperiodic exponent of the power spectrum, long range temporal correlations, and broken detailed balance. A random forest classifier using feature selection was trained to predict poor clinical outcome based on EEG features at 12, 24, 48, 72 and 96 h after trauma. We compared our predictor with the IMPACT score, the best available predictor, based on clinical, radiological and laboratory findings. In addition we created a combined model using EEG as well as the clinical, radiological and laboratory findings. We included hundred-seven patients. The best prediction model using EEG parameters was found at 72 h after trauma with an AUC of 0.82 (0.69-0.92), specificity of 0.83 (0.67-0.99) and sensitivity of 0.74 (0.63-0.93). The IMPACT score predicted poor outcome with an AUC of 0.81 (0.62-0.93), sensitivity of 0.86 (0.74-0.96) and specificity of 0.70 (0.43-0.83). A model using EEG and clinical, radiological and laboratory parameters resulted in a better prediction of poor outcome (p < 0.001) with an AUC of 0.89 (0.72-0.99), sensitivity of 0.83 (0.62-0.93) and specificity of 0.85 (0.75-1.00). EEG features have potential use for predicting clinical outcome and decision making in patients with moderate to severe TBI and provide complementary information to current clinical standards.
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Affiliation(s)
- Prejaas K B Tewarie
- Clinical Neurophysiology Group, University of Twente, Enschede, the Netherlands; Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, the Netherlands; Department of Neurology, Amsterdam UMC/VUmc, Amsterdam, the Netherlands.
| | - Tim M J Beernink
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Carin J Eertman-Meyer
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Alexander D Cornet
- Intensive Care Center, Medisch Spectrum Twente, Enschede, 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, Enschede, the Netherlands
| | - Marleen C Tjepkema-Cloostermans
- Clinical Neurophysiology Group, University of Twente, Enschede, the Netherlands; Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, the Netherlands
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8
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Willems LM, Rosenow F, Knake S, Beuchat I, Siebenbrodt K, Strüber M, Schieffer B, Karatolios K, Strzelczyk A. Repetitive Electroencephalography as Biomarker for the Prediction of Survival in Patients with Post-Hypoxic Encephalopathy. J Clin Med 2022; 11:6253. [PMID: 36362477 PMCID: PMC9658509 DOI: 10.3390/jcm11216253] [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: 10/08/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 09/08/2024] Open
Abstract
Predicting survival in patients with post-hypoxic encephalopathy (HE) after cardiopulmonary resuscitation is a challenging aspect of modern neurocritical care. Here, continuous electroencephalography (cEEG) has been established as the gold standard for neurophysiological outcome prediction. Unfortunately, cEEG is not comprehensively available, especially in rural regions and developing countries. The objective of this monocentric study was to investigate the predictive properties of repetitive EEGs (rEEGs) with respect to 12-month survival based on data for 199 adult patients with HE, using log-rank and multivariate Cox regression analysis (MCRA). A total number of 59 patients (29.6%) received more than one EEG during the first 14 days of acute neurocritical care. These patients were analyzed for the presence of and changes in specific EEG patterns that have been shown to be associated with favorable or poor outcomes in HE. Based on MCRA, an initially normal amplitude with secondary low-voltage EEG remained as the only significant predictor for an unfavorable outcome, whereas all other relevant parameters identified by univariate analysis remained non-significant in the model. In conclusion, rEEG during early neurocritical care may help to assess the prognosis of HE patients if cEEG is not available.
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Affiliation(s)
- Laurent M. Willems
- Department of Neurology and Epilepsy Center Frankfurt Rhine-Main, Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
- LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
| | - Felix Rosenow
- Department of Neurology and Epilepsy Center Frankfurt Rhine-Main, Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
- LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
| | - Susanne Knake
- LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
- Department of Neurology and Epilepsy Center Hessen, Philipps-University Marburg, 35037 Marburg, Germany
| | - Isabelle Beuchat
- Department of Neurology and Epilepsy Center Frankfurt Rhine-Main, Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
- LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
- Department of Neurology, Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne, 1011 Lausanne, Switzerland
| | - Kai Siebenbrodt
- Department of Neurology and Epilepsy Center Frankfurt Rhine-Main, Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
- LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
| | - Michael Strüber
- Department of Neurology and Epilepsy Center Frankfurt Rhine-Main, Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
- LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
| | - Bernhard Schieffer
- Department of Cardiology, Philipps-University Marburg, 35037 Marburg, Germany
| | | | - Adam Strzelczyk
- Department of Neurology and Epilepsy Center Frankfurt Rhine-Main, Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
- LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe-University Frankfurt am Main, 60323 Frankfurt am Main, Germany
- Department of Neurology, Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne, 1011 Lausanne, Switzerland
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9
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Alkhachroum A, Appavu B, Egawa S, Foreman B, Gaspard N, Gilmore EJ, Hirsch LJ, Kurtz P, Lambrecq V, Kromm J, Vespa P, Zafar SF, Rohaut B, Claassen J. Electroencephalogram in the intensive care unit: a focused look at acute brain injury. Intensive Care Med 2022; 48:1443-1462. [PMID: 35997792 PMCID: PMC10008537 DOI: 10.1007/s00134-022-06854-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 07/31/2022] [Indexed: 02/04/2023]
Abstract
Over the past decades, electroencephalography (EEG) has become a widely applied and highly sophisticated brain monitoring tool in a variety of intensive care unit (ICU) settings. The most common indication for EEG monitoring currently is the management of refractory status epilepticus. In addition, a number of studies have associated frequent seizures, including nonconvulsive status epilepticus (NCSE), with worsening secondary brain injury and with worse outcomes. With the widespread utilization of EEG (spot and continuous EEG), rhythmic and periodic patterns that do not fulfill strict seizure criteria have been identified, epidemiologically quantified, and linked to pathophysiological events across a wide spectrum of critical and acute illnesses, including acute brain injury. Increasingly, EEG is not just qualitatively described, but also quantitatively analyzed together with other modalities to generate innovative measurements with possible clinical relevance. In this review, we discuss the current knowledge and emerging applications of EEG in the ICU, including seizure detection, ischemia monitoring, detection of cortical spreading depolarizations, assessment of consciousness and prognostication. We also review some technical aspects and challenges of using EEG in the ICU including the logistics of setting up ICU EEG monitoring in resource-limited settings.
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Affiliation(s)
- Ayham Alkhachroum
- Department of Neurology, University of Miami, Miami, FL, USA
- Department of Neurology, Jackson Memorial Hospital, Miami, FL, USA
| | - Brian Appavu
- Department of Child Health and Neurology, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, USA
- Department of Neurosciences, Phoenix Children's Hospital, Phoenix, AZ, USA
| | - Satoshi Egawa
- Neurointensive Care Unit, Department of Neurosurgery, and Stroke and Epilepsy Center, TMG Asaka Medical Center, Saitama, Japan
| | - Brandon Foreman
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati, 231 Albert Sabin Way, Cincinnati, OH, USA
| | - Nicolas Gaspard
- Department of Neurology, Erasme Hospital, Free University of Brussels, Brussels, Belgium
| | - Emily J Gilmore
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Neurocritical Care and Emergency Neurology, Department of Neurology, Ale University School of Medicine, New Haven, CT, USA
| | - Lawrence J Hirsch
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Pedro Kurtz
- Department of Intensive Care Medicine, D'or Institute for Research and Education, Rio de Janeiro, Brazil
- Neurointensive Care, Paulo Niemeyer State Brain Institute, Rio de Janeiro, Brazil
| | - Virginie Lambrecq
- Department of Clinical Neurophysiology and Epilepsy Unit, AP-HP, Pitié Salpêtrière Hospital, Reference Center for Rare Epilepsies, 75013, Paris, France
| | - Julie Kromm
- Departments of Critical Care Medicine and Clinical Neurosciences, Cumming School of Medicine, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, Calgary, AB, Canada
| | - Paul Vespa
- Brain Injury Research Center, Department of Neurosurgery, University of California, Los Angeles, USA
| | - Sahar F Zafar
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Benjamin Rohaut
- Department of Neurology, Sorbonne Université, Pitié-Salpêtrière-AP-HP and Paris Brain Institute, ICM, Inserm, CNRS, Paris, France
| | - Jan Claassen
- Department of Neurology, Neurological Institute, Columbia University, New York Presbyterian Hospital, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA.
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Hwang J, Cho SM, Ritzl EK. Recent applications of quantitative electroencephalography in adult intensive care units: a comprehensive review. J Neurol 2022; 269:6290-6309. [DOI: 10.1007/s00415-022-11337-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 08/10/2022] [Accepted: 08/11/2022] [Indexed: 10/15/2022]
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11
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Johnsen B, Jeppesen J, Duez CHV. Common patterns of EEG reactivity in post-anoxic coma identified by quantitative analyses. Clin Neurophysiol 2022; 142:143-153. [PMID: 36041343 DOI: 10.1016/j.clinph.2022.07.507] [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: 01/11/2022] [Revised: 06/23/2022] [Accepted: 07/28/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Description of typical kinds of EEG reactivity (EEG-R) in post-anoxic coma using a quantitative method. METHODS Study of 101 out-of-hospital cardiac arrest patients, 71 with good outcome (cerebral performance category scale ≤ 2). EEG was recorded 12-24 hours after cardiac arrest and four noxious, one auditory, and one visual stimulation were applied for 30 seconds each. Individual reference intervals for the power in the delta, theta, alpha, and beta bands were calculated based on six 2-second resting epochs just prior to stimulations. EEG-R in consecutive 2-second epochs after stimulation was expressed in Z-scores. RESULTS EEG-R occurred roughly equally frequent as an increase or as a decrease in EEG activity. Sternal rub and sound stimulation were most provocative with the most pronounced changes as an increase in delta activity 4.5-8.5 seconds after stimulation and a decrease in theta activity 0.5-4.5 seconds after stimulation. These parameters predicted good outcome with an AUC of 0.852 (95 % CI: 0.771-0.932). CONCLUSIONS Quantitative EEG-R is a feasible method for identification of common types of reactivity, for evaluation of stimulation methods, and for prognostication. SIGNIFICANCE This method provides an objective measure of EEG-R revealing knowledge about the nature of EEG-R and its use as a diagnostic tool.
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Affiliation(s)
- Birger Johnsen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Denmark.
| | - Jesper Jeppesen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Denmark
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12
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Neurological Prognostication Using Raw EEG Patterns and Spectrograms of Frontal EEG in Cardiac Arrest Patients. J Clin Neurophysiol 2022; 39:427-433. [DOI: 10.1097/wnp.0000000000000787] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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13
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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 2022; 69:1813-1825. [PMID: 34962860 PMCID: PMC9087641 DOI: 10.1109/tbme.2021.3139007] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [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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14
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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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15
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Lee JH. Early Neuroprognostication Using Frontal Spectrograms in Moderately Sedated Cardiac Arrest Patients. Clin EEG Neurosci 2022; 54:281-288. [PMID: 35043722 DOI: 10.1177/15500594221074888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Introduction. The integrated suppression ratio throughout all electroencephalography (EEG) patterns has rarely been studied. The aim of this study was to evaluate the clinical utility of the suppression ratio and hyperactivity of EEG on spectrograms. Methods. This prospective observational study included 73 cardiac arrest patients. Hardwired frontal EEG monitoring with spectrograms (color density spectral arrays, CDSA) was used to predict neurological outcomes. The mean suppression ratio (MSR) and hyperactivity in the high-frequency band (HHF) in the spectrogram were investigated in moderately sedated patients. Sedative doses were considered to estimate the MSR, which was automatically measured. Results. Using propofol 30 to 40 µg/kg/min and remifentanil 0.1 to 0.15 µg/kg/min, all the patients with an MSR >30% died. At day 2, the MSR in patients with a good outcome was 0%. The cut off values were different as an MSR >30% at day 1 (AUC 0.815) and an MSR >1% at day 2 (AUC 0.891). Of the patients with an MSR ≤30%, HHF was the greatest predictor of a poor outcome (OR 12.858, P = .006). The best predictors of a poor outcome using the spectrogram were suppression ratio (SR) >30% or HHF at day 1 (AUC 0.88) and SR >1% or HHF at day 2 (AUC 0.909). Conclusions. The use of MSR and HHF in frontal spectrograms is convenient and may be successfully employed for early neuroprognostication in moderately sedated cardiac arrest patients. However, spectrograms should be used with electroencephalogram considering the effects of sedatives because of the imperfect detection of electrographic seizures and artifacts.
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Affiliation(s)
- Jae Hoon Lee
- 65368Dong-A University College of Medicine, Busan, Korea
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16
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Vassallo P, Novy J, Zubler F, Schindler K, Alvarez V, Rüegg S, Rossetti AO. EEG spindles integrity in critical care adults. Analysis of a randomized trial. Acta Neurol Scand 2021; 144:655-662. [PMID: 34309006 PMCID: PMC9290497 DOI: 10.1111/ane.13510] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 07/15/2021] [Accepted: 07/18/2021] [Indexed: 01/03/2023]
Abstract
OBJECTIVES Occurrence of EEG spindles has been recently associated with favorable outcome in ICU patients. Available data mostly rely on relatively small patients' samples, particular etiologies, and limited variables ascertainment. We aimed to expand previous findings on a larger dataset, to identify clinical and EEG patterns correlated with spindle occurrence, and explore its prognostic implications. METHODS Retrospective observational study of prospectively collected data from a randomized trial (CERTA, NCT03129438) assessing the relationship of continuous (cEEG) versus repeated routine EEG (rEEG) with outcome in adults with acute consciousness impairment. Spindles were prospectively assessed visually as 12-16Hz activity on fronto-central midline regions, at any time during EEG interventions. Uni- and multivariable analyses explored correlations between spindles occurrence, clinical and EEG variables, and outcome (modified Rankin Scale, mRS; mortality) at 6 months. RESULTS Among the analyzed 364 patients, spindles were independently associated with EEG background reactivity (OR 13.2, 95% CI: 3.11-56.26), and cEEG recording (OR 4.35, 95% CI: 2.5 - 7.69). In the cEEG subgroup (n=182), 33.5% had spindles. They had better FOUR scores (p=0.004), fewer seizures or status epilepticus (p=0.02), and lower mRS (p=0.02). Mortality was reduced (p=0.002), and independently inversely associated with spindle occurrence (OR 0.50, CI 95% 0.25-0.99) and increased EEG background continuity (OR 0.16, 95% CI: 0.07 - 0.41). CONCLUSIONS Besides confirming that spindle activity occurs in up to one third of acutely ill patients and is associated with better outcome, this study shows that cEEG has a higher yield than rEEG in identifying them. Furthermore, it unravels associations with several clinical and EEG features in this clinical setting.
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Affiliation(s)
- Paola Vassallo
- Department of Clinical NeuroscienceLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Jan Novy
- Department of Clinical NeuroscienceLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Frédéric Zubler
- Sleep – Wake – Epilepsy ‐ CenterDepartment of NeurologyInselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Kaspar Schindler
- Sleep – Wake – Epilepsy ‐ CenterDepartment of NeurologyInselspital, Bern University HospitalUniversity of BernBernSwitzerland
| | - Vincent Alvarez
- Department of Clinical NeuroscienceLausanne University Hospital and University of LausanneLausanneSwitzerland
- Department of NeurologyHôpital du ValaisSionSwitzerland
| | - Stephan Rüegg
- Department of NeurologyUniversity Hospital Basel and University of BaselBaselSwitzerland
| | - Andrea O. Rossetti
- Department of Clinical NeuroscienceLausanne University Hospital and University of LausanneLausanneSwitzerland
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Aghaeeaval M, Bendahan N, Shivji Z, McInnis C, Jamzad A, Lomax LB, Shukla G, Mousavi P, Winston GP. Prediction of patient survival following postanoxic coma using EEG data and clinical features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:997-1000. [PMID: 34891456 DOI: 10.1109/embc46164.2021.9629946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Electroencephalography (EEG) is an effective and non-invasive technique commonly used to monitor brain activity and assist in outcome prediction for comatose patients post cardiac arrest. EEG data may demonstrate patterns associated with poor neurological outcome for patients with hypoxic injury. Thus, both quantitative EEG (qEEG) and clinical data contain prognostic information for patient outcome. In this study we use machine learning (ML) techniques, random forest (RF) and support vector machine (SVM) to classify patient outcome post cardiac arrest using qEEG and clinical feature sets, individually and combined. Our ML experiments show RF and SVM perform better using the joint feature set. In addition, we extend our work by implementing a convolutional neural network (CNN) based on time-frequency images derived from EEG to compare with our qEEG ML models. The results demonstrate significant performance improvement in outcome prediction using non-feature based CNN compared to our feature based ML models. Implementation of ML and DL methods in clinical practice have the potential to improve reliability of traditional qualitative assessments for postanoxic coma patients.
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Prognostication of patients in coma after cardiac arrest: Public perspectives. Resuscitation 2021; 169:4-10. [PMID: 34634358 DOI: 10.1016/j.resuscitation.2021.10.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 11/24/2022]
Abstract
AIM To elicit preferences for prognostic information, attitudes towards withdrawal of life-sustaining treatment (WLST) and perspectives on acceptable quality of life after post-anoxic coma within the adult general population of Germany, Italy, the Netherlands and the United States of America. METHODS A web-based survey, consisting of questions on respondent characteristics, perspectives on quality of life, communication of prognostic information, and withdrawal of life-sustaining treatment, was taken by adult respondents recruited from four countries. Statistical analysis included descriptive analysis and chi2-tests for differences between countries. RESULTS In total, 2012 respondents completed the survey. In each country, at least 84% indicated they would prefer to receive early prognostic information. If a poor outcome was predicted with some uncertainty, 37-54% of the respondents indicated that WLST was not to be allowed. A conscious state with severe physical and cognitive impairments was perceived as acceptable quality of life by 17-44% of the respondents. Clear differences between countries exist, including respondents from the U.S. being more likely to allow WLST than respondents from Germany (OR = 1.99, p < 0.001) or the Netherlands (OR = 1.74, p < 0.001) and preferring to stay alive in a conscious state with severe physical and cognitive impairments more than respondents from Italy (OR = 3.76, p < 0.001), Germany (OR = 2.21, p < 0.001), or the Netherlands (OR = 2.39, p < 0.001). CONCLUSIONS Over one-third of the respondents considered WLST unacceptable when there is any remaining prognostic uncertainty. Respondents had a more positive perspective on acceptable quality of life after coma than what is currently considered acceptable in medical literature. This indicates a need for a closer look at the practice of WLST based on prognostic information, to ensure responsible use of novel prognostic tests.
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EEG patterns and their correlations with short- and long-term mortality in patients with hypoxic encephalopathy. Clin Neurophysiol 2021; 132:2851-2860. [PMID: 34598037 DOI: 10.1016/j.clinph.2021.07.026] [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: 01/29/2021] [Revised: 06/29/2021] [Accepted: 07/20/2021] [Indexed: 11/21/2022]
Abstract
OBJECTIVE To analyze the association between electroencephalographic (EEG) patterns and overall, short- and long-term mortality in patients with hypoxic encephalopathy (HE). METHODS Retrospective, mono-center analysis of 199 patients using univariate log-rank tests (LR) and multivariate cox regression (MCR). RESULTS Short-term mortality, defined as death within 30-days post-discharge was 54.8%. Long-term mortality rates were 69.8%, 71.9%, and 72.9%, at 12-, 24-, and 36-months post-HE, respectively. LR revealed a significant association between EEG suppression (SUP) and short-term mortality, and identified low voltage EEG (LV), burst suppression (BSP), periodic discharges (PD) and post-hypoxic status epilepticus (PSE) as well as missing (aBA) or non-reactive background activity (nrBA) as predictors for overall, short- and long-term mortality. MCR indicated SUP, LV, BSP, PD, aBA and nrBA as significantly associated with overall and short-term mortality to varying extents. LV and BSP were significant predictors for long-term mortality in short-term survivors. Rhythmic delta activity, stimulus induced rhythmic, periodic or ictal discharges and sharp waves were not significantly associated with a higher mortality. CONCLUSION The presence of several specific EEG patterns can help to predict overall, short- and long-term mortality in HE patients. SIGNIFICANCE The present findings may help to improve the challenging prognosis estimation in HE patients.
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Association of Standard Electroencephalography Findings With Mortality and Command Following in Mechanically Ventilated Patients Remaining Unresponsive After Sedation Interruption. Crit Care Med 2021; 49:e423-e432. [PMID: 33591021 DOI: 10.1097/ccm.0000000000004874] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
CONTEXT Delayed awakening after sedation interruption is frequent in critically ill patients receiving mechanical ventilation. OBJECTIVES We aimed to investigate the association of standard electroencephalography with mortality and command following in this setting. DESIGN, SETTING, AND PATIENTS In a single-center study, we retrospectively analyzed standard electroencephalography performed in consecutive mechanically ventilated patients remaining unresponsive (comatose/stuporous or unable to follow commands) after sedation interruption. Standard electroencephalography parameters (background activity, continuity, and reactivity) were reassessed by neurophysiologists, blinded to patients' outcome. Patients were categorized during follow-up into three groups based on their best examination as: 1) command following, 2) unresponsive, or 3) deceased. Cause-specific models were used to identify independent standard electroencephalography parameters associated with main outcomes, that is, mortality and command following. Follow-up was right-censored 30 days after standard electroencephalography. MEASUREMENTS AND MAIN RESULTS Main standard electroencephalography parameters recorded in 121 unresponsive patients (median time between sedation interruption and standard electroencephalography: 2 d [interquartile range, 1-4 d]) consisted of a background frequency greater than 4 Hz in 71 (59%), a discontinuous background in 19 (16%), and a preserved reactivity in 98/120 (82%) patients. At 30 days, 66 patients (55%) were command following, nine (7%) were unresponsive, and 46 (38%) had died. In a multivariate analysis adjusted for nonneurologic organ failure, a reactive standard electroencephalography with a background frequency greater than 4 Hz was independently associated with a reduced risk of death (cause-specific hazard ratio, 0.38; CI 95%, 0.16-0.9). By contrast, none of the standard electroencephalography parameters were independently associated with command following. Sensitivity analyses conducted after exclusion of 29 patients with hypoxic brain injury revealed similar findings. CONCLUSIONS In patients remaining unresponsive after sedation interruption, a pattern consisting of a reactive standard electroencephalography with a background frequency greater than 4 Hz was associated with decreased odds of death. None of the standard electroencephalography parameters were independently associated with command following.
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Automated Assessment of Brain CT After Cardiac Arrest-An Observational Derivation/Validation Cohort Study. Crit Care Med 2021; 49:e1212-e1222. [PMID: 34374503 DOI: 10.1097/ccm.0000000000005198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Objectives Prognostication of outcome is an essential step in defining therapeutic goals after cardiac arrest. Gray-white-matter ratio obtained from brain CT can predict poor outcome. However, manual placement of regions of interest is a potential source of error and interrater variability. Our objective was to assess the performance of poor outcome prediction by automated quantification of changes in brain CTs after cardiac arrest. Design Observational, derivation/validation cohort study design. Outcome was determined using the Cerebral Performance Category upon hospital discharge. Poor outcome was defined as death or unresponsive wakefulness syndrome/coma. CTs were automatically decomposed using coregistration with a brain atlas. Setting ICUs at a large, academic hospital with circulatory arrest center. Patients We identified 433 cardiac arrest patients from a large previously established database with brain CTs within 10 days after cardiac arrest. Interventions None. Measurements and Main Results Five hundred sixteen brain CTs were evaluated (derivation cohort n = 309, validation cohort n = 207). Patients with poor outcome had significantly lower radiodensities in gray matter regions. Automated GWR_si (putamen/posterior limb of internal capsule) was performed with an area under the curve of 0.86 (95%-CI: 0.80-0.93) for CTs taken later than 24 hours after cardiac arrest (similar performance in the validation cohort). Poor outcome (Cerebral Performance Category 4-5) was predicted with a specificity of 100% (95% CI, 87-100%, derivation; 88-100%, validation) at a threshold of less than 1.10 and a sensitivity of 49% (95% CI, 36-58%, derivation) and 38% (95% CI, 27-50%, validation) for CTs later than 24 hours after cardiac arrest. Sensitivity and area under the curve were lower for CTs performed within 24 hours after cardiac arrest. Conclusions Automated gray-white-matter ratio from brain CT is a promising tool for prediction of poor neurologic outcome after cardiac arrest with high specificity and low-to-moderate sensitivity. Prediction by gray-white-matter ratio at the basal ganglia level performed best. Sensitivity increased considerably for CTs performed later than 24 hours after cardiac arrest.
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Nolan JP, Sandroni C, Böttiger BW, Cariou A, Cronberg T, Friberg H, Genbrugge C, Haywood K, Lilja G, Moulaert VRM, Nikolaou N, Olasveengen TM, Skrifvars MB, Taccone F, Soar J. Postreanimationsbehandlung. Notf Rett Med 2021. [DOI: 10.1007/s10049-021-00892-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Nolan JP, Sandroni C, Böttiger BW, Cariou A, Cronberg T, Friberg H, Genbrugge C, Haywood K, Lilja G, Moulaert VRM, Nikolaou N, Olasveengen TM, Skrifvars MB, Taccone F, Soar J. European Resuscitation Council and European Society of Intensive Care Medicine guidelines 2021: post-resuscitation care. Intensive Care Med 2021; 47:369-421. [PMID: 33765189 PMCID: PMC7993077 DOI: 10.1007/s00134-021-06368-4] [Citation(s) in RCA: 464] [Impact Index Per Article: 154.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/08/2021] [Indexed: 12/13/2022]
Abstract
The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation care guidelines for adults, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include the post-cardiac arrest syndrome, diagnosis of cause of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation and organ donation.
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Affiliation(s)
- Jerry P. Nolan
- University of Warwick, Warwick Medical School, Coventry, CV4 7AL UK
- Royal United Hospital, Bath, BA1 3NG UK
| | - 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, Rome, Italy
| | - Bernd W. Böttiger
- Department of Anaesthesiology and Intensive Care Medicine, University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Alain Cariou
- Cochin University Hospital (APHP) and University of Paris (Medical School), Paris, France
| | - Tobias Cronberg
- Department of Clinical Sciences, Neurology, Lund University, Skane University Hospital, Lund, Sweden
| | - Hans Friberg
- Department of Clinical Sciences, Anaesthesia and Intensive Care Medicine, Lund University, Skane University Hospital, Lund, Sweden
| | - Cornelia Genbrugge
- Acute Medicine Research Pole, Institute of Experimental and Clinical Research (IREC), Université Catholique de Louvain, Brussels, Belgium
- Emergency Department, University Hospitals Saint-Luc, Brussels, Belgium
| | - Kirstie Haywood
- Warwick Research in Nursing, Division of Health Sciences, Warwick Medical School, University of Warwick, Room A108, Coventry, CV4 7AL UK
| | - Gisela Lilja
- Department of Clinical Sciences Lund, Neurology, Lund University, Skane University Hospital, Lund, Sweden
| | - Véronique R. M. Moulaert
- Department of Rehabilitation Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Nikolaos Nikolaou
- Cardiology Department, Konstantopouleio General Hospital, Athens, Greece
| | - Theresa Mariero Olasveengen
- Department of Anesthesiology, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Markus B. Skrifvars
- Department of Emergency Care and Services, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Fabio Taccone
- Department of Intensive Care, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik, 808, 1070 Brussels, Belgium
| | - Jasmeet Soar
- Southmead Hospital, North Bristol NHS Trust, Bristol, BS10 5NB UK
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Nolan JP, Sandroni C, Böttiger BW, Cariou A, Cronberg T, Friberg H, Genbrugge C, Haywood K, Lilja G, Moulaert VRM, Nikolaou N, Mariero Olasveengen T, Skrifvars MB, Taccone F, Soar J. European Resuscitation Council and European Society of Intensive Care Medicine Guidelines 2021: Post-resuscitation care. Resuscitation 2021; 161:220-269. [PMID: 33773827 DOI: 10.1016/j.resuscitation.2021.02.012] [Citation(s) in RCA: 375] [Impact Index Per Article: 125.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation care guidelines for adults, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include the post-cardiac arrest syndrome, diagnosis of cause of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation, and organ donation.
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Affiliation(s)
- Jerry P Nolan
- University of Warwick, Warwick Medical School, Coventry CV4 7AL, UK; Royal United Hospital, Bath, BA1 3NG, UK.
| | - 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, Rome, Italy
| | - Bernd W Böttiger
- University Hospital of Cologne, Kerpener Straße 62, D-50937 Cologne, Germany
| | - Alain Cariou
- Cochin University Hospital (APHP) and University of Paris (Medical School), Paris, France
| | - Tobias Cronberg
- Department of Clinical Sciences, Neurology, Lund University, Skane University Hospital, Lund, Sweden
| | - Hans Friberg
- Department of Clinical Sciences, Anaesthesia and Intensive Care Medicine, Lund University, Skane University Hospital, Lund, Sweden
| | - Cornelia Genbrugge
- Acute Medicine Research Pole, Institute of Experimental and Clinical Research (IREC) Université Catholique de Louvain, Brussels, Belgium; Emergency Department, University Hospitals Saint-Luc, Brussels, Belgium
| | - Kirstie Haywood
- Warwick Research in Nursing, Room A108, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Gisela Lilja
- Lund University, Skane University Hospital, Department of Clinical Sciences Lund, Neurology, Lund, Sweden
| | - Véronique R M Moulaert
- University of Groningen, University Medical Center Groningen, Department of Rehabilitation Medicine, Groningen, The Netherlands
| | - Nikolaos Nikolaou
- Cardiology Department, Konstantopouleio General Hospital, Athens, Greece
| | - Theresa Mariero Olasveengen
- Department of Anesthesiology, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Norway
| | - Markus B Skrifvars
- Department of Emergency Care and Services, University of Helsinki and Helsinki University Hospital, Finland
| | - Fabio Taccone
- Department of Intensive Care, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik, 808, 1070 Brussels, Belgium
| | - Jasmeet Soar
- Southmead Hospital, North Bristol NHS Trust, Bristol BS10 5NB, UK
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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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26
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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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27
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Sandroni C, D'Arrigo S, Cacciola S, Hoedemaekers CWE, Kamps MJA, Oddo M, Taccone FS, Di Rocco A, Meijer FJA, Westhall E, Antonelli M, Soar J, Nolan JP, Cronberg T. Prediction of poor neurological outcome in comatose survivors of cardiac arrest: a systematic review. Intensive Care Med 2020; 46:1803-1851. [PMID: 32915254 PMCID: PMC7527362 DOI: 10.1007/s00134-020-06198-w] [Citation(s) in RCA: 180] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 07/15/2020] [Indexed: 12/17/2022]
Abstract
Purpose To assess the ability of clinical examination, blood biomarkers, electrophysiology, or neuroimaging assessed within 7 days from return of spontaneous circulation (ROSC) to predict poor neurological outcome, defined as death, vegetative state, or severe disability (CPC 3–5) at hospital discharge/1 month or later, in comatose adult survivors from cardiac arrest (CA). Methods PubMed, EMBASE, Web of Science, and the Cochrane Database of Systematic Reviews (January 2013–April 2020) were searched. Sensitivity and false-positive rate (FPR) for each predictor were calculated. Due to heterogeneities in recording times, predictor thresholds, and definition of some predictors, meta-analysis was not performed. Results Ninety-four studies (30,200 patients) were included. Bilaterally absent pupillary or corneal reflexes after day 4 from ROSC, high blood values of neuron-specific enolase from 24 h after ROSC, absent N20 waves of short-latency somatosensory-evoked potentials (SSEPs) or unequivocal seizures on electroencephalogram (EEG) from the day of ROSC, EEG background suppression or burst-suppression from 24 h after ROSC, diffuse cerebral oedema on brain CT from 2 h after ROSC, or reduced diffusion on brain MRI at 2–5 days after ROSC had 0% FPR for poor outcome in most studies. Risk of bias assessed using the QUIPS tool was high for all predictors. Conclusion In comatose resuscitated patients, clinical, biochemical, neurophysiological, and radiological tests have a potential to predict poor neurological outcome with no false-positive predictions within the first week after CA. Guidelines should consider the methodological concerns and limited sensitivity for individual modalities. (PROSPERO CRD42019141169) Electronic supplementary material The online version of this article (10.1007/s00134-020-06198-w) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"- IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy.,Institute of Anesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Sonia D'Arrigo
- Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"- IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy.
| | - Sofia Cacciola
- Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"- IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy
| | | | - Marlijn J A Kamps
- Intensive Care Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Mauro Oddo
- Department of Intensive Care Medicine, University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Fabio S Taccone
- Department of Intensive Care, Erasme Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Arianna Di Rocco
- Department of Public Health and Infectious Disease, Sapienza University, Rome, Italy
| | - Frederick J A Meijer
- Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Erik Westhall
- Department of ClinicalSciences, Clinical Neurophysiology, Lund University, Skane University Hospital, Lund, Sweden
| | - Massimo Antonelli
- Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"- IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy.,Institute of Anesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Jasmeet Soar
- Critical Care Unit, Southmead Hospital, North Bristol NHS Trust, Bristol, UK
| | - Jerry P Nolan
- Department of Anaesthesia and Intensive Care Medicine, Royal United Hospital, Bath, UK
| | - Tobias Cronberg
- Department of Clinical Sciences Lund, Neurology, Lund University, Skane University Hospital, Lund, Sweden
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Comanducci A, Boly M, Claassen J, De Lucia M, Gibson RM, Juan E, Laureys S, Naccache L, Owen AM, Rosanova M, Rossetti AO, Schnakers C, Sitt JD, Schiff ND, Massimini M. Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group. Clin Neurophysiol 2020; 131:2736-2765. [PMID: 32917521 DOI: 10.1016/j.clinph.2020.07.015] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 07/06/2020] [Accepted: 07/26/2020] [Indexed: 12/13/2022]
Abstract
The analysis of spontaneous EEG activity and evoked potentialsis a cornerstone of the instrumental evaluation of patients with disorders of consciousness (DoC). Thepast few years have witnessed an unprecedented surge in EEG-related research applied to the prediction and detection of recovery of consciousness after severe brain injury,opening up the prospect that new concepts and tools may be available at the bedside. This paper provides a comprehensive, critical overview of bothconsolidated and investigational electrophysiological techniquesfor the prognostic and diagnostic assessment of DoC.We describe conventional clinical EEG approaches, then focus on evoked and event-related potentials, and finally we analyze the potential of novel research findings. In doing so, we (i) draw a distinction between acute, prolonged and chronic phases of DoC, (ii) attempt to relate both clinical and research findings to the underlying neuronal processes and (iii) discuss technical and conceptual caveats.The primary aim of this narrative review is to bridge the gap between standard and emerging electrophysiological measures for the detection and prediction of recovery of consciousness. The ultimate scope is to provide a reference and common ground for academic researchers active in the field of neurophysiology and clinicians engaged in intensive care unit and rehabilitation.
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Affiliation(s)
- A Comanducci
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
| | - M Boly
- Department of Neurology and Department of Psychiatry, University of Wisconsin, Madison, USA; Wisconsin Institute for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin-Madison, Madison, USA
| | - J Claassen
- Department of Neurology, Columbia University Medical Center, New York Presbyterian Hospital, New York, NY, USA
| | - M De Lucia
- Laboratoire de Recherche en Neuroimagerie, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - R M Gibson
- The Brain and Mind Institute and the Department of Physiology and Pharmacology, Western Interdisciplinary Research Building, N6A 5B7 University of Western Ontario, London, Ontario, Canada
| | - E Juan
- Wisconsin Institute for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin-Madison, Madison, USA; Amsterdam Brain and Cognition, Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
| | - S Laureys
- Coma Science Group, Centre du Cerveau, GIGA-Consciousness, University and University Hospital of Liège, 4000 Liège, Belgium; Fondazione Europea per la Ricerca Biomedica Onlus, Milan 20063, Italy
| | - L Naccache
- Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France; Sorbonne Université, UPMC Université Paris 06, Faculté de Médecine Pitié-Salpêtrière, Paris, France
| | - A M Owen
- The Brain and Mind Institute and the Department of Physiology and Pharmacology, Western Interdisciplinary Research Building, N6A 5B7 University of Western Ontario, London, Ontario, Canada
| | - M Rosanova
- Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy; Fondazione Europea per la Ricerca Biomedica Onlus, Milan 20063, Italy
| | - A O Rossetti
- Neurology Service, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - C Schnakers
- Research Institute, Casa Colina Hospital and Centers for Healthcare, Pomona, CA, USA
| | - J D Sitt
- Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - N D Schiff
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, 1300 York Avenue, New York, NY 10065, USA
| | - M Massimini
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy; Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy
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Griffith JL, Tomko ST, Guerriero RM. Continuous Electroencephalography Monitoring in Critically Ill Infants and Children. Pediatr Neurol 2020; 108:40-46. [PMID: 32446643 DOI: 10.1016/j.pediatrneurol.2020.04.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 12/15/2022]
Abstract
Continuous video electroencephalography (CEEG) monitoring of critically ill infants and children has expanded rapidly in recent years. Indications for CEEG include evaluation of patients with altered mental status, characterization of paroxysmal events, and detection of electrographic seizures, including monitoring of patients with limited neurological examination or conditions that put them at high risk for electrographic seizures (e.g., cardiac arrest or extracorporeal membrane oxygenation cannulation). Depending on the inclusion criteria and clinical characteristics of the population studied, the percentage of pediatric patients with electrographic seizures varies from 7% to 46% and with electrographic status epilepticus from 1% to 23%. There is also evidence that epileptiform and background CEEG patterns may provide important information about prognosis in certain clinical populations. Quantitative EEG techniques are emerging as a tool to enhance the value of CEEG to provide real-time bedside data for management and prognosis. Continued research is needed to understand the clinical value of seizure detection and identification of other CEEG patterns on the outcomes of critically ill infants and children.
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Affiliation(s)
- Jennifer L Griffith
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri.
| | - Stuart T Tomko
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Réjean M Guerriero
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
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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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Background Frequency Patterns in Standard Electroencephalography as an Early Prognostic Tool in Out-of-Hospital Cardiac Arrest Survivors Treated with Targeted Temperature Management. J Clin Med 2020; 9:jcm9041113. [PMID: 32295020 PMCID: PMC7230199 DOI: 10.3390/jcm9041113] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 03/31/2020] [Accepted: 04/08/2020] [Indexed: 12/27/2022] Open
Abstract
We investigated the prognostic value of standard electroencephalography, a 30-min recording using 21 electrodes on the scalp, during the early post-cardiac arrest period, and evaluated the performance of electroencephalography findings combined with other clinical features for predicting favourable outcomes in comatose out-of-hospital cardiac arrest (OHCA) survivors treated with targeted temperature management (TTM). This observational registry-based study was conducted at a tertiary care hospital in Korea using the data of all consecutive adult non-traumatic comatose OHCA survivors who underwent standard electroencephalography during TTM between 2010 and 2018. The primary outcome was a 6-month favourable neurological outcome (Cerebral Performance Category score of 1 or 2). Among 170 comatose OHCA survivors with median electroencephalography time of 22 h, a 6-month favourable neurologic outcome was observed in 34.1% (58/170). After adjusting other clinical characteristics, an electroencephalography background with dominant alpha and theta waves had the highest odds ratio of 13.03 (95% confidence interval, 4.69–36.22) in multivariable logistic analysis. A combination of other clinical features (age < 65 years, initial shockable rhythm, resuscitation duration < 20 min) with an electroencephalography background with dominant alpha and theta waves increased predictive performance for favourable neurologic outcomes with a high specificity of up to 100%. A background with dominant alpha and theta waves in standard electroencephalography during TTM could be a simple and early favourable prognostic finding in comatose OHCA survivors.
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Forgacs PB, Devinsky O, Schiff ND. Independent Functional Outcomes after Prolonged Coma following Cardiac Arrest: A Mechanistic Hypothesis. Ann Neurol 2020; 87:618-632. [PMID: 31994749 PMCID: PMC7393600 DOI: 10.1002/ana.25690] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 01/23/2020] [Accepted: 01/23/2020] [Indexed: 01/26/2023]
Abstract
OBJECTIVE Survivors of prolonged (>2 weeks) post-cardiac arrest (CA) coma are expected to remain permanently disabled. We aimed to investigate 3 outlier patients who ultimately achieved independent functional outcomes after prolonged post-CA coma to identify electroencephalographic (EEG) markers of their recovery potential. For validation purposes, we also aimed to evaluate these markers in an independent cohort of post-CA patients. METHODS We identified 3 patients with late recovery from coma (17-37 days) following CA who recovered to functionally independent behavioral levels. We performed spectral power analyses of available EEGs during prominent burst suppression patterns (BSP) present in all 3 patients. Using identical methods, we also assessed the relationship of intraburst spectral power and outcomes in a prospectively enrolled cohort of post-CA patients. We performed chart reviews of common clinical, imaging, and EEG prognostic variables and clinical outcomes for all patients. RESULTS All 3 patients with late recovery from coma lacked evidence of overwhelming cortical injury but demonstrated prominent BSP on EEG. Spectral analyses revealed a prominent theta (~4-7Hz) feature dominating the bursts during BSP in these patients. In the prospective cohort, similar intraburst theta spectral features were evident in patients with favorable outcomes; patients with BSP and unfavorable outcomes showed either no features, transient burst features, or decreasing intraburst frequencies with time. INTERPRETATION BSP with theta (~4-7Hz) peak intraburst spectral power after CA may index a recovery potential. We discuss our results in the context of optimizing metabolic substrate availability and stimulating the corticothalamic system during recovery from prolonged post-CA coma. ANN NEUROL 2020;87:618-632.
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Affiliation(s)
- Peter B. Forgacs
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York, NY 10065, USA
- Department of Neurology, Weill Cornell Medical College, New York, NY 10065, USA
- The Rockefeller University, New York, NY 10065, USA
| | | | - Nicholas D. Schiff
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York, NY 10065, USA
- Department of Neurology, Weill Cornell Medical College, New York, NY 10065, USA
- The Rockefeller University, New York, NY 10065, USA
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Barbella G, Novy J, Marques-Vidal P, Oddo M, Rossetti AO. Prognostic role of EEG identical bursts in patients after cardiac arrest: Multimodal correlation. Resuscitation 2020; 148:140-144. [PMID: 32004660 DOI: 10.1016/j.resuscitation.2020.01.017] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 01/13/2020] [Accepted: 01/18/2020] [Indexed: 12/19/2022]
Abstract
AIMS EEG burst-suppression (BS) heralds poor outcome after cardiac arrest (CA). Within this pattern, identical bursts (IB) have been suggested to be absolutely specific, in isolation. We assessed IB prevalence and their added predictive value for poor outcome in a multimodal prognostic approach. METHODS We retrospectively analyzed a registry of comatose adults with CA (April 2011-February 2019), undergoing EEG at 5-36 h and 36-72 h. SB and IB were visually assessed. Cerebral Performance Categories (CPC) at 3 months were dichotomized as "good" (CPC 1-2), or "poor" (CPC 3-5). Sensitivity, specificity, positive, negative predictive values of BS and IB for poor outcome were calculated. A multimodal prognostic score was created assigning one point each to unreactive and epileptiform EEG, pupillary light reflex and SSEPs absence, NSE > 75 μg/l. In a second score, IB were added; predictive performances were compared using Receiver Operating Characteristic (ROC) curves. RESULTS Of 522 patients, 147 (28%) had BS in any EEG (10 [7%] had good outcome and 129 [88%] died). Of them, 53/147 (36%, 10% of total) showed IB, 47/53 (89%) of which within 36 h. IB were 100% specific for poor outcome, and associated with higher serum NSE than BS. However, there was no significant difference between the scores with and without IB for CPC 3-5 (p = 0.116). CONCLUSION IB occur in 10% of patients after CA. In our multimodal context, IB, albeit being very specific for poor outcome, seem redundant with other predictors.
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Affiliation(s)
- Giuseppina Barbella
- Neurology Unit, San Gerardo Hospital, Monza, Italy; School of Medicine and Surgery and Milan-Center for Neuroscience (NeuroMI), University of Milano-Bicocca, Milan, Italy; Department of Neurology, Lausanne University Hospital (CHUV) and University of Lausanne, Switzerland
| | - Jan Novy
- Department of Neurology, Lausanne University Hospital (CHUV) and University of Lausanne, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV) and University of Lausanne, Switzerland
| | - Mauro Oddo
- Department of Adult Intensive Care, Lausanne University Hospital (CHUV) and University of Lausanne, Switzerland
| | - Andrea O Rossetti
- Department of Neurology, Lausanne University Hospital (CHUV) and University of Lausanne, Switzerland.
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Glimmerveen AB, Ruijter BJ, Keijzer HM, Tjepkema-Cloostermans MC, van Putten MJ, Hofmeijer J. Association between somatosensory evoked potentials and EEG in comatose patients after cardiac arrest. Clin Neurophysiol 2019; 130:2026-2031. [DOI: 10.1016/j.clinph.2019.08.022] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 06/21/2019] [Accepted: 08/18/2019] [Indexed: 12/30/2022]
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Ruijter BJ, van Putten MJ, van den Bergh WM, Tromp SC, Hofmeijer J. Propofol does not affect the reliability of early EEG for outcome prediction of comatose patients after cardiac arrest. Clin Neurophysiol 2019; 130:1263-1270. [DOI: 10.1016/j.clinph.2019.04.707] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 03/11/2019] [Accepted: 04/03/2019] [Indexed: 12/01/2022]
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Jonas S, Rossetti AO, Oddo M, Jenni S, Favaro P, Zubler F. EEG-based outcome prediction after cardiac arrest with convolutional neural networks: Performance and visualization of discriminative features. Hum Brain Mapp 2019; 40:4606-4617. [PMID: 31322793 DOI: 10.1002/hbm.24724] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 06/24/2019] [Accepted: 07/01/2019] [Indexed: 12/11/2022] Open
Abstract
Prognostication for comatose patients after cardiac arrest is a difficult but essential task. Currently, visual interpretation of electroencephalogram (EEG) is one of the main modality used in outcome prediction. There is a growing interest in computer-assisted EEG interpretation, either to overcome the possible subjectivity of visual interpretation, or to identify complex features of the EEG signal. We used a one-dimensional convolutional neural network (CNN) to predict functional outcome based on 19-channel-EEG recorded from 267 adult comatose patients during targeted temperature management after CA. The area under the receiver operating characteristic curve (AUC) on the test set was 0.885. Interestingly, model architecture and fine-tuning only played a marginal role in classification performance. We then used gradient-weighted class activation mapping (Grad-CAM) as visualization technique to identify which EEG features were used by the network to classify an EEG epoch as favorable or unfavorable outcome, and also to understand failures of the network. Grad-CAM showed that the network relied on similar features than classical visual analysis for predicting unfavorable outcome (suppressed background, epileptiform transients). This study confirms that CNNs are promising models for EEG-based prognostication in comatose patients, and that Grad-CAM can provide explanation for the models' decision-making, which is of utmost importance for future use of deep learning models in a clinical setting.
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Affiliation(s)
- Stefan Jonas
- Computer Vision Group, Department of Computer Science, University of Bern, Bern, Switzerland
| | - Andrea O Rossetti
- Department of Neurology, University Hospital (CHUV) & University of Lausanne, Lausanne, Switzerland
| | - Mauro Oddo
- Department of Intensive Care Medicine, University Hospital (CHUV) & University of Lausanne, Lausanne, Switzerland
| | - Simon Jenni
- Computer Vision Group, Department of Computer Science, University of Bern, Bern, Switzerland
| | - Paolo Favaro
- Computer Vision Group, Department of Computer Science, University of Bern, Bern, Switzerland
| | - Frederic Zubler
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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Ruijter BJ, Tjepkema-Cloostermans MC, Tromp SC, van den Bergh WM, Foudraine NA, Kornips FHM, Drost G, Scholten E, Bosch FH, Beishuizen A, van Putten MJAM, Hofmeijer J. Early electroencephalography for outcome prediction of postanoxic coma: A prospective cohort study. Ann Neurol 2019; 86:203-214. [PMID: 31155751 PMCID: PMC6771891 DOI: 10.1002/ana.25518] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 05/28/2019] [Accepted: 05/31/2019] [Indexed: 02/03/2023]
Abstract
Objective To provide evidence that early electroencephalography (EEG) allows for reliable prediction of poor or good outcome after cardiac arrest. Methods In a 5‐center prospective cohort study, we included consecutive, comatose survivors of cardiac arrest. Continuous EEG recordings were started as soon as possible and continued up to 5 days. Five‐minute EEG epochs were assessed by 2 reviewers, independently, at 8 predefined time points from 6 hours to 5 days after cardiac arrest, blinded for patients’ actual condition, treatment, and outcome. EEG patterns were categorized as generalized suppression (<10 μV), synchronous patterns with ≥50% suppression, continuous, or other. Outcome at 6 months was categorized as good (Cerebral Performance Category [CPC] = 1–2) or poor (CPC = 3–5). Results We included 850 patients, of whom 46% had a good outcome. Generalized suppression and synchronous patterns with ≥50% suppression predicted poor outcome without false positives at ≥6 hours after cardiac arrest. Their summed sensitivity was 0.47 (95% confidence interval [CI] = 0.42–0.51) at 12 hours and 0.30 (95% CI = 0.26–0.33) at 24 hours after cardiac arrest, with specificity of 1.00 (95% CI = 0.99–1.00) at both time points. At 36 hours or later, sensitivity for poor outcome was ≤0.22. Continuous EEG patterns at 12 hours predicted good outcome, with sensitivity of 0.50 (95% CI = 0.46–0.55) and specificity of 0.91 (95% CI = 0.88–0.93); at 24 hours or later, specificity for the prediction of good outcome was <0.90. Interpretation EEG allows for reliable prediction of poor outcome after cardiac arrest, with maximum sensitivity in the first 24 hours. Continuous EEG patterns at 12 hours after cardiac arrest are associated with good recovery. ANN NEUROL 2019;86:203–214
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Affiliation(s)
- Barry J Ruijter
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede
| | | | - Selma C Tromp
- Departments of Neurology and Clinical Neurophysiology, St Antonius Hospital, Nieuwegein
| | - Walter M van den Bergh
- Department of Critical Care, University Medical Center Groningen, University of Groningen, Groningen
| | | | | | - Gea Drost
- Departments of Neurology and Neurosurgery, University Medical Center Groningen, University of Groningen, Groningen
| | - Erik Scholten
- Department of Intensive Care, St Antonius Hospital, Nieuwegein
| | - Frank H Bosch
- Department of Intensive Care, Rijnstate Hospital, Arnhem
| | | | - Michel J A M van Putten
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede.,Departments of Neurology and Clinical Neurophysiology, Medical Spectrum Twente, Enschede
| | - Jeannette Hofmeijer
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede.,Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands
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Spectral Content of Electroencephalographic Burst-Suppression Patterns May Reflect Neuronal Recovery in Comatose Post-Cardiac Arrest Patients. J Clin Neurophysiol 2019; 36:119-126. [PMID: 30422916 DOI: 10.1097/wnp.0000000000000536] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
PURPOSE To assess the potential biologic significance of variations in burst-suppression patterns (BSPs) after cardiac arrest in relation to recovery of consciousness. In the context of recent theoretical models of BSP, bursting frequency may be representative of underlying network dynamics; discontinuous activation of membrane potential during impaired cellular energetics may promote neuronal rescue. METHODS We reviewed a database of 73 comatose post-cardiac arrest patients who underwent therapeutic hypothermia to assess for the presence of BSP and clinical outcomes. In a subsample of patients with BSP (n = 14), spectral content of burst and suppression periods were quantified using multitaper method. RESULTS Burst-suppression pattern was seen in 45/73 (61%) patients. Comparable numbers of patients with (31.1%) and without (35.7%) BSP regained consciousness by the time of hospital discharge. In addition, in two unique cases, BSP initially resolved and then spontaneously reemerged after completion of therapeutic hypothermia and cessation of sedative medications. Both patients recovered consciousness. Spectral analysis of bursts in all patients regaining consciousness (n = 6) showed a prominent theta frequency (5-7 Hz) feature, but not in age-matched patients with induced BSP who did not recover consciousness (n = 8). CONCLUSIONS The prognostic implications of BSP after hypoxic brain injury may vary based on the intrinsic properties of the underlying brain state itself. The presence of theta activity within bursts may index potential viability of neuronal networks underlying recovery of consciousness; emergence of spontaneous BSP in some cases may indicate an innate neuroprotective mechanism. This study highlights the need for better characterization of various BSP patterns after cardiac arrest.
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