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Sansevere AJ, Janatti A, DiBacco ML, Cavan K, Rotenberg A. Background EEG Suppression Ratio for Early Detection of Cerebral Injury in Pediatric Cardiac Arrest. Neurocrit Care 2024:10.1007/s12028-023-01920-0. [PMID: 38302644 DOI: 10.1007/s12028-023-01920-0] [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: 08/10/2023] [Accepted: 12/05/2023] [Indexed: 02/03/2024]
Abstract
BACKGROUND Our objective was to assess the utility of the 1-h suppression ratio (SR) as a biomarker of cerebral injury and neurologic prognosis after cardiac arrest (CA) in the pediatric hospital setting. METHODS Prospectively, we reviewed data from children presenting after CA and monitored by continuous electroencephalography (cEEG). Patients aged 1 month to 21 years were included. The SR, a quantitative measure of low-voltage cEEG (≤ 3 µV) content, was dichotomized as present or absent if there was > 0% suppression for one continuous hour. A multivariate logistic regression analysis was performed including age, sex, type of CA (i.e., in-hospital or out-of-hospital), and the presence of SR as a predictor of global anoxic cerebral injury as confirmed by magnetic resonance imaging (MRI). RESULTS We included 84 patients with a median age of 4 years (interquartile range 0.9-13), 64% were male, and 49% (41/84) had in-hospital CA. Cerebral injury was seen in 50% of patients, of whom 65% had global injury. One-hour SR presence, independent of amount, predicted cerebral injury with 81% sensitivity (95% confidence interval (CI) (66-91%) and 98% specificity (95% CI 88-100%). Multivariate logistic regression analyses indicated that SR was a significant predictor of both cerebral injury (β = 6.28, p < 0.001) and mortality (β = 3.56, p < 0.001). CONCLUSIONS The SR a sensitive and specific marker of anoxic brain injury and post-CA mortality in the pediatric population. Once detected in the post-CA setting, the 1-h SR may be a useful threshold finding for deployment of early neuroprotective strategies prior or for prompting diagnostic neuroimaging.
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Affiliation(s)
- Arnold J Sansevere
- Division of Epilepsy and Neurophysiology, Boston Children's Hospital, Boston, MA, USA.
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA.
- Division of Epilepsy, Department of Neurology, Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20001, USA.
| | - Ali Janatti
- Division of Epilepsy and Neurophysiology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - Melissa L DiBacco
- Division of Epilepsy and Neurophysiology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - Kelly Cavan
- Division of Epilepsy and Neurophysiology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - Alexander Rotenberg
- Division of Epilepsy and Neurophysiology, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
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2
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Xu LB, Hampton S, Fischer D. Neuroimaging in Disorders of Consciousness and Recovery. Phys Med Rehabil Clin N Am 2024; 35:51-64. [PMID: 37993193 DOI: 10.1016/j.pmr.2023.06.017] [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: 11/24/2023]
Abstract
There is a clinical need for more accurate diagnosis and prognostication in patients with disorders of consciousness (DoC). There are several neuroimaging modalities that enable detailed, quantitative assessment of structural and functional brain injury, with demonstrated diagnostic and prognostic value. Additionally, longitudinal neuroimaging studies have hinted at quantifiable structural and functional neuroimaging biomarkers of recovery, with potential implications for the management of DoC.
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Affiliation(s)
- Linda B Xu
- Department of Neurology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
| | - Stephen Hampton
- Department of Physical Medicine and Rehabilitation, University of Pennsylvania, 1800 Lombard Street, Philadelphia, PA 19146, USA
| | - David Fischer
- Department of Neurology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
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3
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Park JS, Kim EY, You Y, Min JH, Jeong W, Ahn HJ, In YN, Lee IH, Kim JM, Kang C. Combination strategy for prognostication in patients undergoing post-resuscitation care after cardiac arrest. Sci Rep 2023; 13:21880. [PMID: 38072906 PMCID: PMC10711008 DOI: 10.1038/s41598-023-49345-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 12/07/2023] [Indexed: 12/18/2023] Open
Abstract
This study investigated the prognostic performance of combination strategies using a multimodal approach in patients treated after cardiac arrest. Prospectively collected registry data were used for this retrospective analysis. Poor outcome was defined as a cerebral performance category of 3-5 at 6 months. Predictors of poor outcome were absence of ocular reflexes (PR/CR) without confounding factors, a highly malignant pattern on the most recent electroencephalography, defined as suppressed background with or without periodic discharges and burst-suppression, high neuron-specific enolase (NSE) after 48 h, and diffuse injury on imaging studies (computed tomography or diffusion-weighted imaging [DWI]) at 72-96 h. The prognostic performances for poor outcomes were analyzed for sensitivity and specificity. A total of 130 patients were included in the analysis. Of these, 68 (52.3%) patients had poor outcomes. The best prognostic performance was observed with the combination of absent PR/CR, high NSE, and diffuse injury on DWI [91.2%, 95% confidence interval (CI) 80.7-97.1], whereas the combination strategy of all available predictors did not improve prognostic performance (87.8%, 95% CI 73.8-95.9). Combining three of the predictors may improve prognostic performance and be more efficient than adding all tests indiscriminately, given limited medical resources.
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Affiliation(s)
- Jung Soo Park
- Department of Emergency Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon, Republic of Korea
| | - Eun Young Kim
- Department of Neurology, Chungnam National University Sejong Hospital, 20, Bodeum 7-ro, Sejong, Republic of Korea
| | - Yeonho You
- Department of Emergency Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon, Republic of Korea
| | - Jin Hong Min
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon, Republic of Korea
| | - Wonjoon Jeong
- Department of Emergency Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon, Republic of Korea
| | - Hong Joon Ahn
- Department of Emergency Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon, Republic of Korea
| | - Yong Nam In
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon, Republic of Korea
| | - In Ho Lee
- Department of Radiology, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon, Republic of Korea
- Department of Radiology, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon, Republic of Korea
| | - Jae Moon Kim
- Department of Neurology, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon, Republic of Korea
| | - Changshin Kang
- Department of Emergency Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon, Republic of Korea.
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon, Republic of Korea.
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4
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Bevers MB. Refining the continuum of neurologic prognosis - Predicting brain death after cardiac arrest. Resuscitation 2023; 192:109990. [PMID: 37805059 DOI: 10.1016/j.resuscitation.2023.109990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/09/2023]
Affiliation(s)
- Matthew B Bevers
- Division of Neurocritical Care, Brigham and Women's Hospital, Boston, MA, United States
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5
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Kumar A, Ridha M, Claassen J. Prognosis of consciousness disorders in the intensive care unit. Presse Med 2023; 52:104180. [PMID: 37805070 PMCID: PMC10995112 DOI: 10.1016/j.lpm.2023.104180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 10/03/2023] [Indexed: 10/09/2023] Open
Abstract
Assessments of consciousness are a critical part of prognostic algorithms for critically ill patients suffering from severe brain injuries. There have been significant advances in the field of coma science over the past two decades, providing clinicians with more advanced and precise tools for diagnosing and prognosticating disorders of consciousness (DoC). Advanced neuroimaging and electrophysiological techniques have vastly expanded our understanding of the biological mechanisms underlying consciousness, and have helped identify new states of consciousness. One of these, termed cognitive motor dissociation, can predict functional recovery at 1 year post brain injury, and is present in up to 15-20% of patients with DoC. In this chapter, we review several tools that are used to predict DoC, describing their strengths and limitations, from the neurological examination to advanced imaging and electrophysiologic techniques. We also describe multimodal assessment paradigms that can be used to identify covert consciousness and thus help recognize patients with the potential for future recovery and improve our prognostication practices.
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Affiliation(s)
- Aditya Kumar
- Department of Neurology, Columbia University Medical Center, New York-Presbyterian Hospital, New York, NY, USA
| | - Mohamed Ridha
- Department of Neurology, Columbia University Medical Center, New York-Presbyterian Hospital, New York, NY, USA
| | - Jan Claassen
- Department of Neurology, Columbia University Medical Center, New York-Presbyterian Hospital, New York, NY, USA.
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Calabrese E, Gandhi S, Shih J, Otero M, Randazzo D, Hemphill C, Huie R, Talbott JF, Amorim E. Parieto-Occipital Injury on Diffusion MRI Correlates with Poor Neurologic Outcome following Cardiac Arrest. AJNR Am J Neuroradiol 2023; 44:254-260. [PMID: 36797027 PMCID: PMC10187825 DOI: 10.3174/ajnr.a7779] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 01/03/2023] [Indexed: 02/18/2023]
Abstract
BACKGROUND AND PURPOSE MR imaging of the brain provides unbiased neuroanatomic evaluation of brain injury and is useful for neurologic prognostication following cardiac arrest. Regional analysis of diffusion imaging may provide additional prognostic value and help reveal the neuroanatomic underpinnings of coma recovery. The purpose of this study was to evaluate global, regional, and voxelwise differences in diffusion-weighted MR imaging signal in patients in a coma after cardiac arrest. MATERIALS AND METHODS We retrospectively analyzed diffusion MR imaging data from 81 subjects who were comatose for >48 hours following cardiac arrest. Poor outcome was defined as the inability to follow simple commands at any point during hospitalization. ADC differences between groups were evaluated across the whole brain, locally by using voxelwise analysis and regionally by using ROI-based principal component analysis. RESULTS Subjects with poor outcome had more severe brain injury as measured by lower average whole-brain ADC (740 [SD, 102] × 10-6 mm2/s versus 833 [SD, 23] × 10-6 mm2/s, P < .001) and larger average volumes of tissue with ADC below 650 × 10-6 mms/s (464 [SD, 469] mL versus 62 [SD, 51] mL, P < .001). Voxelwise analysis showed lower ADC in the bilateral parieto-occipital areas and perirolandic cortices for the poor outcome group. ROI-based principal component analysis showed an association between lower ADC in parieto-occipital regions and poor outcome. CONCLUSIONS Brain injury affecting the parieto-occipital region measured with quantitative ADC analysis was associated with poor outcomes after cardiac arrest. These results suggest that injury to specific brain regions may influence coma recovery.
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Affiliation(s)
- E Calabrese
- From the Department of Radiology and Biomedical Imaging (E.C., S.G., J.F.T.)
| | - S Gandhi
- From the Department of Radiology and Biomedical Imaging (E.C., S.G., J.F.T.)
- Department of Radiology and Biomedical Imaging (S.G., J.F.T., E.A.), Zuckerberg San Francisco General Hospital, San Francisco, California
| | - J Shih
- Department of Neurology (J.S., M.O., D.R., C.H., E.A.), Weill Institute for Neurosciences
| | - M Otero
- Department of Neurology (J.S., M.O., D.R., C.H., E.A.), Weill Institute for Neurosciences
| | - D Randazzo
- Department of Neurology (J.S., M.O., D.R., C.H., E.A.), Weill Institute for Neurosciences
| | - C Hemphill
- Department of Neurology (J.S., M.O., D.R., C.H., E.A.), Weill Institute for Neurosciences
| | - R Huie
- Department of Neurological Surgery (R.H.), University of California, San Francisco, San Francisco, California
| | - J F Talbott
- From the Department of Radiology and Biomedical Imaging (E.C., S.G., J.F.T.)
- Department of Radiology and Biomedical Imaging (S.G., J.F.T., E.A.), Zuckerberg San Francisco General Hospital, San Francisco, California
| | - E Amorim
- Department of Neurology (J.S., M.O., D.R., C.H., E.A.), Weill Institute for Neurosciences
- Department of Radiology and Biomedical Imaging (S.G., J.F.T., E.A.), Zuckerberg San Francisco General Hospital, San Francisco, California
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7
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Free water corrected diffusion tensor imaging discriminates between good and poor outcomes of comatose patients after cardiac arrest. Eur Radiol 2023; 33:2139-2148. [PMID: 36418623 PMCID: PMC9935650 DOI: 10.1007/s00330-022-09245-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/26/2022] [Accepted: 10/16/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Approximately 50% of comatose patients after cardiac arrest never regain consciousness. Cerebral ischaemia may lead to cytotoxic and/or vasogenic oedema, which can be detected by diffusion tensor imaging (DTI). Here, we evaluate the potential value of free water corrected mean diffusivity (MD) and fractional anisotropy (FA) based on DTI, for the prediction of neurological recovery of comatose patients after cardiac arrest. METHODS A total of 50 patients after cardiac arrest were included in this prospective cohort study in two Dutch hospitals. DTI was obtained 2-4 days after cardiac arrest. Outcome was assessed at 6 months, dichotomised as poor (cerebral performance category 3-5; n = 20) or good (n = 30) neurological outcome. We calculated the whole brain mean MD and FA and compared between patients with good and poor outcomes. In addition, we compared a preliminary prediction model based on clinical parameters with or without the addition of MD and FA. RESULTS We found significant differences between patients with good and poor outcome of mean MD (good: 726 [702-740] × 10-6 mm2/s vs. poor: 663 [575-736] × 10-6 mm2/s; p = 0.01) and mean FA (0.30 ± 0.03 vs. 0.28 ± 0.03; p = 0.03). An exploratory prediction model combining clinical parameters, MD and FA increased the sensitivity for reliable prediction of poor outcome from 60 to 85%, compared to the model containing clinical parameters only, but confidence intervals are overlapping. CONCLUSIONS Free water-corrected MD and FA discriminate between patients with good and poor outcomes after cardiac arrest and hold the potential to add to multimodal outcome prediction. KEY POINTS • Whole brain mean MD and FA differ between patients with good and poor outcome after cardiac arrest. • Free water-corrected MD can better discriminate between patients with good and poor outcome than uncorrected MD. • A combination of free water-corrected MD (sensitive to grey matter abnormalities) and FA (sensitive to white matter abnormalities) holds potential to add to the prediction of outcome.
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8
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Keijzer HM, Lange PA, Meijer FJ, Tonino BA, Blans MJ, Klijn CJ, Hoedemaekers CW, Hofmeijer J, Helmich RC. MRI markers of brain network integrity relate to neurological outcome in postanoxic coma. Neuroimage Clin 2022; 36:103171. [PMID: 36058165 PMCID: PMC9446009 DOI: 10.1016/j.nicl.2022.103171] [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: 02/10/2022] [Revised: 08/18/2022] [Accepted: 08/23/2022] [Indexed: 12/14/2022]
Abstract
AIM Current multimodal approaches leave approximately half of the comatose patients after cardiac arrest with an indeterminate prognosis. Here we investigated whether early MRI markers of brain network integrity can distinguish between comatose patients with a good versus poor neurological outcome six months later. METHODS We performed a prospective cohort study in 48 patients after cardiac arrest submitted in a comatose state to the Intensive Care Unit of two Dutch hospitals. MRI was performed at three days after cardiac arrest, including resting state functional MRI and diffusion-tensor imaging (DTI). Resting state fMRI was used to quantify functional connectivity within ten resting-state networks, and DTI to assess mean diffusivity (MD) in these same networks. We contrasted two groups of patients, those with good (n = 29, cerebral performance category 1-2) versus poor (n = 19, cerebral performance category 3-5) outcome at six months. Mutual associations between functional connectivity, MD, and clinical outcome were studied. RESULTS Patients with good outcome show higher within-network functional connectivity (fMRI) and higher MD (DTI) than patients with poor outcome across 8/10 networks, most prominent in the default mode network, salience network, and visual network. While the anatomical distribution of outcome-related changes was similar for functional connectivity and MD, the pattern of inter-individual differences was very different: functional connectivity showed larger inter-individual variability in good versus poor outcome, while the opposite was observed for MD. Exploratory analyses suggested that it is possible to define network-specific cut-off values that could help in outcome prediction: (1) high functional connectivity and high MD, associated with good outcome; (2) low functional connectivity and low MD, associated with poor outcome; (3) low functional connectivity and high MD, associated with uncertain outcome. DISCUSSION Resting-state functional connectivity and mean diffusivity-three days after cardiac arrest are strongly associated with neurological recovery-six months later in a complementary fashion. The combination of fMRI and MD holds potential to improve prediction of outcome.
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Affiliation(s)
- Hanneke M. Keijzer
- Department of Neurology, Rijnstate Hospital, 6800 TA Arnhem, the Netherlands,Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, 6500 HC Nijmegen, the Netherlands,Corresponding author at: Department of Neurology, Rijnstate Hospital, PO box 9555 TA Arnhem, the Netherlands.
| | - Puck A.M. Lange
- Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, 6500 HC Nijmegen, the Netherlands
| | - Frederick J.A. Meijer
- Department of Medical Imaging, Radboud University Medical Centre, 6500 HC Nijmegen, the Netherlands
| | - Bart A.R. Tonino
- Department of Radiology, Rijnstate Hospital, 6800 TA Arnhem, the Netherlands
| | - Michiel J. Blans
- Department of Intensive Care Medicine, Rijnstate Hospital, Arnhem, the Netherlands
| | - Catharina J.M. Klijn
- Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, 6500 HC Nijmegen, the Netherlands
| | - Cornelia W.E. Hoedemaekers
- Department of Intensive Care Medicine, Radboud University Medical Centre, 6500 HC Nijmegen, the Netherlands
| | - Jeannette Hofmeijer
- Department of Neurology, Rijnstate Hospital, 6800 TA Arnhem, the Netherlands,Department of Clinical Neurophysiology, University of Twente, Faculty of Science and Technology, 7522 NB Enschede, the Netherlands
| | - Rick C. Helmich
- Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, 6500 HC Nijmegen, the Netherlands
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Fischer D, Newcombe V, Fernandez-Espejo D, Snider SB. Applications of Advanced MRI to Disorders of Consciousness. Semin Neurol 2022; 42:325-334. [PMID: 35790201 DOI: 10.1055/a-1892-1894] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Disorder of consciousness (DoC) after severe brain injury presents numerous challenges to clinicians, as the diagnosis, prognosis, and management are often uncertain. Magnetic resonance imaging (MRI) has long been used to evaluate brain structure in patients with DoC. More recently, advances in MRI technology have permitted more detailed investigations of the brain's structural integrity (via diffusion MRI) and function (via functional MRI). A growing literature has begun to show that these advanced forms of MRI may improve our understanding of DoC pathophysiology, facilitate the identification of patient consciousness, and improve the accuracy of clinical prognostication. Here we review the emerging evidence for the application of advanced MRI for patients with DoC.
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Affiliation(s)
- David Fischer
- Division of Neurocritical Care, Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Virginia Newcombe
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Davinia Fernandez-Espejo
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Samuel B Snider
- Division of Neurocritical Care, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts
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10
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Prognosis After Cardiac Arrest: The Additional Value of DWI and FLAIR to EEG. Neurocrit Care 2022; 37:302-313. [DOI: 10.1007/s12028-022-01498-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 03/28/2022] [Indexed: 10/18/2022]
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Snider SB, Fischer D, McKeown ME, Cohen AL, Schaper FLWVJ, Amorim E, Fox MD, Scirica B, Bevers MB, Lee JW. Regional Distribution of Brain Injury After Cardiac Arrest: Clinical and Electrographic Correlates. Neurology 2022; 98:e1238-e1247. [PMID: 35017304 PMCID: PMC8967331 DOI: 10.1212/wnl.0000000000013301] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 12/27/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Disorders of consciousness, EEG background suppression, and epileptic seizures are associated with poor outcome after cardiac arrest. Our objective was to identify the distribution of diffusion MRI-measured anoxic brain injury after cardiac arrest and to define the regional correlates of disorders of consciousness, EEG background suppression, and seizures. METHODS We analyzed patients from a single-center database of unresponsive patients who underwent diffusion MRI after cardiac arrest (n = 204). We classified each patient according to recovery of consciousness (command following) before discharge, the most continuous EEG background (burst suppression vs continuous), and the presence or absence of seizures. Anoxic brain injury was measured with the apparent diffusion coefficient (ADC) signal. We identified ADC abnormalities relative to controls without cardiac arrest (n = 48) and used voxel lesion symptom mapping to identify regional associations with disorders of consciousness, EEG background suppression, and seizures. We then used a bootstrapped lasso regression procedure to identify robust, multivariate regional associations with each outcome variable. Last, using area under receiver operating characteristic curves, we then compared the classification ability of the strongest regional associations to that of brain-wide summary measures. RESULTS Compared to controls, patients with cardiac arrest demonstrated ADC signal reduction that was most significant in the occipital lobes. Disorders of consciousness were associated with reduced ADC most prominently in the occipital lobes but also in deep structures. Regional injury more accurately classified patients with disorders of consciousness than whole-brain injury. Background suppression mapped to a similar set of brain regions, but regional injury could no better classify patients than whole-brain measures. Seizures were less common in patients with more severe anoxic injury, particularly in those with injury to the lateral temporal white matter. DISCUSSION Anoxic brain injury was most prevalent in posterior cerebral regions, and this regional pattern of injury was a better predictor of disorders of consciousness than whole-brain injury measures. EEG background suppression lacked a specific regional association, but patients with injury to the temporal lobe were less likely to have seizures. Regional patterns of anoxic brain injury are relevant to the clinical and electrographic sequelae of cardiac arrest and may hold importance for prognosis. CLASSIFICATION OF EVIDENCE This study provides Class IV evidence that disorders of consciousness after cardiac arrest are associated with widely lower ADC values on diffusion MRI and are most strongly associated with reductions in occipital ADC.
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Affiliation(s)
- Samuel B Snider
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
| | - David Fischer
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Morgan E McKeown
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Alexander Li Cohen
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Frederic L W V J Schaper
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Edilberto Amorim
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Michael D Fox
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Benjamin Scirica
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Matthew B Bevers
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Jong Woo Lee
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
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Critical care EEG standardized nomenclature in clinical practice: Strengths, limitations, and outlook on the example of prognostication after cardiac arrest. Clin Neurophysiol Pract 2022; 6:149-154. [PMID: 35112033 PMCID: PMC8790140 DOI: 10.1016/j.cnp.2021.03.002] [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: 11/01/2020] [Revised: 01/08/2021] [Accepted: 03/03/2021] [Indexed: 11/21/2022] Open
Abstract
Optimal use of the ACNS nomenclature implies integration of clinical information. Knowledge of pathophysiological mechanisms of EEG patterns may help interpretation. Standardized therapeutic procedures for critical care patients are needed.
We discuss the achievements of the ACNS critical care EEG nomenclature proposed in 2013 and, from a clinical angle, outline some limitations regarding translation into treatment implications. While the recently proposed updated 2021 version of the nomenclature will probable improve some uncertainty areas, a refined understanding of the mechanisms at the origin of the EEG patterns, and a multimodal integration of the nomenclature to the clinical context may help improving the rationale supporting therapeutic procedures. We illustrate these aspects on prognostication after cardiac arrest.
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Key Words
- ACNS, American Clinical Neurophysiology Society
- American Clinical Neurophysiology Society (ACNS) Standardized Terminology
- BIRD, Brief potentially ictal rhythmic discharge
- BS, Burst suppression
- Burst suppression
- CA, Cardiac arrest
- Cardiac arrest (CA)
- DWI, diffusion-weighted MRI
- ESI, electric source imaging
- GPD
- GPD, generalized periodic discharge
- GRDA, generalized rhythmic delta activity
- ICU, Intensive care unit
- ICU-EEG, intensive care unit-electroencephalography
- IIC, Ictal-Interictal Continuum
- Ictal-Interictal Continuum
- LPD, Lateralized periodic discharge
- MEG, Magneto-electroencephalography
- NCSE, Non-Convulsive Status Epilepticus
- NSE, Serum neuron-specific enolase
- PET, Positron emission tomography
- Prognostication assessment
- SE, Status epilepticus
- SPECT, Single Photon Emission Computed Tomography
- SSEP, Somatosensory evoked potentials
- WLST, Withdraw of life sustaining treatment
- fMRI, functional MRI
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Smith AE, Ganninger AP, Mian AY, Friess SH, Guerriero RM, Guilliams KP. Magnetic Resonance Imaging Adds Prognostic Value to EEG After Pediatric Cardiac Arrest. Resuscitation 2022; 173:91-100. [PMID: 35227820 PMCID: PMC9001021 DOI: 10.1016/j.resuscitation.2022.02.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 02/11/2022] [Accepted: 02/20/2022] [Indexed: 10/19/2022]
Abstract
AIM To investigate how combined electrographic and radiologic data inform outcomes in children after cardiac arrest. METHODS Retrospective observational study of children admitted to the pediatric intensive care unit (PICU) of a tertiary children's hospital with diagnosis of cardiac arrest from 2009 to 2016. The first 20 min of electroencephalogram (EEG) background was blindly scored. Presence and location of magnetic resonance imaging (MRI) diffusion-weighted image (DWI) abnormalities were correlated with T2-weighted signal. Outcomes were categorized using Pediatric Cerebral Performance Category (PCPC) scores at hospital discharge, with "poor outcome" reflecting a PCPC score of 4-6. Logistic regression models examined the association of EEG and MRI variables with outcome. RESULTS 41 children met inclusion criteria and had both post-arrest EEG monitoring within 72 hours after ROSC and brain MRI performed within 8 days. Among the 19 children with poor outcome, 10 children did not survive to discharge. Severely abnormal EEG background (p < 0.0001) and any diffusion restriction (p < 0.0001) were associated with poor outcome. The area under the ROC curve (AUC) for identifying outcome based on EEG background alone was 0.86, which improved to 0.94 with combined EEG and MRI data (p = 0.02). CONCLUSION Diffusion abnormalities on MRI within 8 days after ROSC add to the prognostic value of EEG background in children surviving cardiac arrest.
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14
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Zheng WL, Amorim E, Jing J, Wu O, Ghassemi M, Lee JW, Sivaraju A, Pang T, Herman ST, Gaspard N, Ruijter BJ, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM, Westover MB. Predicting Neurological Outcome from Electroencephalogram Dynamics in Comatose Patients after Cardiac Arrest with Deep Learning. IEEE Trans Biomed Eng 2021; 69:1813-1825. [PMID: 34962860 PMCID: PMC9087641 DOI: 10.1109/tbme.2021.3139007] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. METHODS We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation. RESULTS The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). CONCLUSIONS AND SIGNIFICANCE These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.
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15
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Zheng WL, Amorim E, Jing J, Ge W, Hong S, Wu O, Ghassemi M, Lee JW, Sivaraju A, Pang T, Herman ST, Gaspard N, Ruijter BJ, Sun J, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM, Westover MB. Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks. Resuscitation 2021; 169:86-94. [PMID: 34699925 PMCID: PMC8692444 DOI: 10.1016/j.resuscitation.2021.10.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/19/2021] [Accepted: 10/20/2021] [Indexed: 11/19/2022]
Abstract
OBJECTIVE Electroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest coma recovery likelihood. METHODS We developed a multiscale DNN combining convolutional neural networks (CNN) and recurrent neural networks (long short-term memory [LSTM]) using EEG and demographic information (age, gender, shockable rhythm) from a multicenter cohort of 1,038 cardiac arrest patients. The CNN learns EEG feature representations while the multiscale LSTM captures short-term and long-term EEG dynamics on multiple time scales. Poor outcome is defined as a Cerebral Performance Category (CPC) score of 3-5 and good outcome as CPC score 1-2 at 3-6 months after cardiac arrest. Performance is evaluated using area under the receiver operating characteristic curve (AUC) and calibration error. RESULTS Model performance increased with EEG duration, with AUC increasing from 0.83 (95% Confidence Interval [CI] 0.79-0.87 at 12h to 0.91 (95%CI 0.88-0.93) at 66h. Sensitivity of good and poor outcome prediction was 77% and 75% at a specificity of 90%, respectively. Sensitivity of poor outcome was 50% at a specificity of 99%. Predicted probability was well matched to the observation frequency of poor outcomes, with a calibration error of 0.11 [0.09-0.14]. CONCLUSIONS These results demonstrate that incorporating EEG evolution over time improves the accuracy of neurologic outcome prediction for patients with coma after cardiac arrest.
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Affiliation(s)
- Wei-Long Zheng
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Edilberto Amorim
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Shenda Hong
- Department of Computer Science, University of Illinois at Urbana Champaign, Champaign, IL, USA
| | - Ona Wu
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mohammad Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Adithya Sivaraju
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Trudy Pang
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Nicolas Gaspard
- Department of Neurology, Université Libre de Bruxelles, Brussels, Belgium
| | - Barry J Ruijter
- Department of Clinical Neurophysiology, University of Twente, Enschede, the Netherlands
| | - Jimeng Sun
- Department of Computer Science, University of Illinois at Urbana Champaign, Champaign, IL, USA
| | - Marleen C Tjepkema-Cloostermans
- Departments of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, the Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands
| | - Jeannette Hofmeijer
- Department of Clinical Neurophysiology, University of Twente, Enschede, the Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands
| | - Michel J A M van Putten
- Department of Clinical Neurophysiology, University of Twente, Enschede, the Netherlands; Departments of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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16
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Neuroprognostication after Cardiac Arrest: Who Recovers? Who Progresses to Brain Death? Semin Neurol 2021; 41:606-618. [PMID: 34619784 DOI: 10.1055/s-0041-1733789] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Approximately 15% of deaths in developed nations are due to sudden cardiac arrest, making it the most common cause of death worldwide. Though high-quality cardiopulmonary resuscitation has improved overall survival rates, the majority of survivors remain comatose after return of spontaneous circulation secondary to hypoxic ischemic injury. Since the advent of targeted temperature management, neurologic recovery has improved substantially, but the majority of patients are left with neurologic deficits ranging from minor cognitive impairment to persistent coma. Of those who survive cardiac arrest, but die during their hospitalization, some progress to brain death and others die after withdrawal of life-sustaining treatment due to anticipated poor neurologic prognosis. Here, we discuss considerations neurologists must make when asked, "Given their recent cardiac arrest, how much neurologic improvement do we expect for this patient?"
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17
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Vogelsong MA, May T, Agarwal S, Cronberg T, Dankiewicz J, Dupont A, Friberg H, Hand R, McPherson J, Mlynash M, Mooney M, Nielsen N, O'Riordan A, Patel N, Riker RR, Seder DB, Soreide E, Stammet P, Xiong W, Hirsch KG. Influence of sex on survival, neurologic outcomes, and neurodiagnostic testing after out-of-hospital cardiac arrest. Resuscitation 2021; 167:66-75. [PMID: 34363853 DOI: 10.1016/j.resuscitation.2021.07.037] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 07/18/2021] [Accepted: 07/22/2021] [Indexed: 10/20/2022]
Abstract
AIM Previous studies evaluating the relationship between sex and post-resuscitation care and outcomes following out-of-hospital cardiac arrest (OHCA) are conflicting. We investigated the association between sex and outcomes as well as neurodiagnostic testing in a prospective multicenter international registry of patients admitted to intensive care units following OHCA. METHODS OHCA survivors enrolled in the International Cardiac Arrest Registry (INTCAR) from 2012-2017 were included. We assessed the independent association between sex and survival to hospital discharge, good neurologic outcome (Cerebral Performance Category 1 or 2), neurodiagnostic testing, and withdrawal of life-sustaining therapy (WLST). RESULTS Of 2,407 eligible patients, 809 (33.6%) were women. Baseline characteristics differed by sex, with less bystander CPR and initial shockable rhythms among women. Women were less likely to survive to hospital discharge, however significance abated following adjusted analysis (30.1% vs 42.7%, adjusted OR 0.85, 95% CI 0.67-1.08). Women were less likely to have good neurologic outcome at discharge (21.4% vs 34.0%, adjusted OR 0.74, 95% CI 0.57-0.96) and at six months post-arrest (16.7% vs 29.4%, adjusted OR 0.73, 95% CI 0.54-0.98) that persisted after adjustment. Neuroimaging (75.5% vs 74.3%, p=0.54) and other neurophysiologic testing (78.8% vs 78.6%, p=0.91) was similar across sex. Women were more likely to undergo WLST (55.6% vs 42.8%, adjusted OR 1.35, 95% CI 1.09-1.66). CONCLUSIONS Women with cardiac arrest have lower odds of good neurologic outcomes and higher odds of WLST, despite comparable rates of neurodiagnostic testing and after controlling for baseline differences in clinical characteristics and cardiac arrest features.
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Affiliation(s)
- Melissa A Vogelsong
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, United States.
| | - Teresa May
- Department of Critical Care Services, Maine Medical Center, Portland, ME, United States
| | - Sachin Agarwal
- Department of Neurology, Columbia University Medical Center/New York Presbyterian Hospital, New York City, NY, United States Tobias Cronberg - Department of Clinical Sciences, Neurology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Tobias Cronberg
- Department of Clinical Sciences, Neurology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Josef Dankiewicz
- Department of Cardiology, Skåne University Hospital, Lund, Sweden
| | - Allison Dupont
- Department of Cardiology, Northside Cardiovascular Institute, Atlanta, GA, United States
| | - Hans Friberg
- Department of Clinical Sciences, Intensive and Perioperative Care, Lund University, Skåne University Hospital, Malmö, Sweden
| | | | - John McPherson
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Michael Mlynash
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Michael Mooney
- Department of Cardiology, Minneapolis Heart Institute, Abbot North-Western Hospital, Minneapolis, MN, United States
| | - Niklas Nielsen
- Department of Anesthesiology and Intensive Care, Helsingborg Hospital, Helsingborg, Sweden, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Andrea O'Riordan
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Nainesh Patel
- Department of Cardiology, Lehigh Valley Health Network, Allentown, PA, United States
| | - Richard R Riker
- Department of Critical Care Services, Maine Medical Center, Portland, ME, United States
| | - David B Seder
- Department of Critical Care Services, Maine Medical Center, Portland, ME, United States
| | - Eldar Soreide
- Critical Care and Anaesthesiology Research Group, Stavanger University Hospital, Stavanger, Norway, Department Clinical Medicine, University of Bergen, Bergen, Norway
| | - Pascal Stammet
- Medical and Health Department, Luxembourg Fire and Rescue Corps, Luxembourg, Luxembourg
| | - Wei Xiong
- Department of Neurology, Case Western Reserve University School of Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Karen G Hirsch
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
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Abstract
PURPOSE OF REVIEW In the study of brain-injured patients with disorders of consciousness (DoC), structural and functional MRI seek to provide insights into the neural correlates of consciousness, identify neurophysiologic signatures of covert consciousness, and identify biomarkers for recovery of consciousness. RECENT FINDINGS Cortical volume, white matter volume and integrity, and structural connectivity across many grey and white matter regions have been shown to vary with level of awareness in brain-injured patients. Resting-state functional connectivity (rs-FC) within and between canonical cortical networks also correlates with DoC patients' level of awareness. Stimulus-based and motor-imagery fMRI paradigms have identified some behaviorally unresponsive DoC patients with cortical processing and activation patterns that mirror healthy controls. Emerging techniques like dynamic rs-FC have begun to identify temporal trends in brain-wide connectivity that may represent novel neural correlates of consciousness. SUMMARY Structural and functional MRI will continue to advance our understanding of brain regions supporting human consciousness. Measures of regional and global white matter integrity and rs-FC in particular networks have shown significant improvement over clinical features in identifying acute and chronic DoC patients likely to recover awareness. As they are refined, functional MRI paradigms may additionally provide opportunities for interacting with behaviorally unresponsive patients.
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Wouters A, Scheldeman L, Plessers S, Peeters R, Cappelle S, Demaerel P, Van Paesschen W, Ferdinande B, Dupont M, Dens J, Janssens S, Ameloot K, Lemmens R. Added Value of Quantitative Apparent Diffusion Coefficient Values for Neuroprognostication After Cardiac Arrest. Neurology 2021; 96:e2611-e2618. [PMID: 33837117 DOI: 10.1212/wnl.0000000000011991] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 02/26/2021] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To test the prognostic value of brain MRI in addition to clinical and electrophysiologic variables in patients post-cardiac arrest (CA), we explored data from the randomized Neuroprotect Post-CA trial (NCT02541591). METHODS In this trial, brain MRIs were prospectively obtained. We calculated receiver operating characteristic (ROC) curves for the average apparent diffusion coefficient (ADC) value and percentage of brain voxels with an ADC value <650 × 10-6 mm2/s and <450 × 10-6 mm2/s. We constructed multivariable logistic regression models with clinical characteristics, EEG, somatosensory evoked potentials (SSEP), and ADC value as independent variables to predict good neurologic recovery. RESULTS In 79/102 patients, MRI data were available and in 58/79 patients all other data were available. At 180 days post-CA, 25/58 (43%) patients had good neurologic recovery. In univariable analysis of all tested MRI measures, average ADC value in the postcentral cortex had the highest accuracy to predict good neurologic recovery, with an area under the ROC curve (AUC) of 0.78. In the most optimal multivariable model, which also included corneal reflexes and EEG, this measure remained an independent predictor of good neurologic recovery (AUC 0.96, false-positive 27%). This model provided a more accurate prediction compared to the most optimal combination of EEG, corneal reflexes, and SSEP (p = 0.03). CONCLUSIONS Adding information on brain MRI in a multivariable model may improve the prediction of good neurologic recovery in patients post-CA. CLASSIFICATION OF EVIDENCE This study provides Class III evidence that MRI ADC features predict neurologic recovery in patients post-CA.
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Affiliation(s)
- Anke Wouters
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium.
| | - Lauranne Scheldeman
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Sam Plessers
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Ronald Peeters
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Sarah Cappelle
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Philippe Demaerel
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Wim Van Paesschen
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Bert Ferdinande
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Matthias Dupont
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Jo Dens
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Stefan Janssens
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Koen Ameloot
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Robin Lemmens
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
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Neuron-specific enolase and neuroimaging for prognostication after cardiac arrest treated with targeted temperature management. PLoS One 2020; 15:e0239979. [PMID: 33002033 PMCID: PMC7529296 DOI: 10.1371/journal.pone.0239979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 09/17/2020] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Prognostication after cardiac arrest (CA) needs a multimodal approach, but the optimal method is not known. We tested the hypothesis that the combination of neuron-specific enolase (NSE) and neuroimaging could improve outcome prediction after CA treated with targeted temperature management (TTM). METHODS A retrospective observational cohort study was performed on patients who underwent at least one NSE measurement between 48 and 72 hr; received both a brain computed tomography (CT) scan within 24 hr and diffusion-weighted magnetic resonance imaging (DW-MRI) within 7 days after return of spontaneous circulation (ROSC); and were treated with TTM after out-of-hospital CA between 2009 and 2017 at the Seoul St. Mary's Hospital in Korea. The primary outcome was a poor neurological outcome at 6 months after CA, defined as a cerebral performance category of 3-5. RESULTS A total of 109 subjects underwent all three tests and were ultimately included in this study. Thirty-four subjects (31.2%) experienced good neurological outcomes at 6 months after CA. The gray matter to white matter attenuation ratio (GWR) was weakly correlated with the mean apparent diffusion coefficient (ADC), PV400 and NSE (Spearman's rho: 0.359, -0.362 and -0.263, respectively). NSE was strongly correlated with the mean ADC and PV400 (Spearman's rho: -0.623 and 0.666, respectively). Serum NSE had the highest predictive value among the single parameters (area under the curve (AUC) 0.912, sensitivity 70.7% for maintaining 100% specificity). The combination of a DWI parameter (mean ADC or PV400) and NSE had better prognostic performance than the combination of the CT parameter (GWR) and NSE. The addition of the GWR to a DWI parameter and NSE did not improve the prediction of neurological outcomes. CONCLUSION The GWR (≤ 24 hr) is weakly correlated with the mean ADC (≤ 7 days) and NSE (highest between 48 and 72 hr). The combination of a DWI parameter and NSE has better prognostic performance than the combination of the GWR and NSE. The addition of the GWR to a DWI parameter and NSE does not improve the prediction of neurological outcomes after CA treatment with TTM.
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21
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Weathered NR. Cardiac and Pulmonary Disorders and the Nervous System. Continuum (Minneap Minn) 2020; 26:556-576. [PMID: 32487896 DOI: 10.1212/con.0000000000000859] [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: 11/15/2022]
Abstract
PURPOSE OF REVIEW This article reviews the neurologic complications encountered with cardiac and pulmonary disorders, specifically focusing on endocarditis, cardiac arrest, heart failure, hypercapnia, hypoxia, and cystic fibrosis. As neurologic dysfunction is one of the most frequent complications of these diseases and may even be the presenting symptom, it is important to be familiar with these complications to foster early recognition and intervention. RECENT FINDINGS Advances have been made in the identification of which patients can safely undergo valvular surgery for treatment of infective endocarditis in the setting of stroke, which, ideally, will minimize the risk of recurrent stroke in these patients. Additionally, technologic advances are improving our ability to use a multimodal approach for prognostication after cardiac arrest. SUMMARY The neurologic complications from the described disorders range from cerebrovascular complications to encephalitis, cognitive impairment, sleep-disordered breathing, headache, and increased intracranial pressure leading to coma or even death. Given the severity of these symptoms, it is paramount that neurologists be closely involved in the care of patients with neurologic complications from cardiac and pulmonary disorders.
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Beuchat I, Sivaraju A, Amorim E, Gilmore EJ, Dunet V, Rossetti AO, Westover MB, Hsu L, Scirica BM, Silva D, Tang K, Lee JW. MRI-EEG correlation for outcome prediction in postanoxic myoclonus: A multicenter study. Neurology 2020; 95:e335-e341. [PMID: 32482841 DOI: 10.1212/wnl.0000000000009610] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 01/27/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To examine the prognostic ability of the combination of EEG and MRI in identifying patients with good outcome in postanoxic myoclonus (PAM) after cardiac arrest (CA). METHODS Adults with PAM who had an MRI within 20 days after CA were identified in 4 prospective CA registries. The primary outcome measure was coma recovery to command following by hospital discharge. Clinical examination included brainstem reflexes and motor activity. EEG was assessed for best background continuity, reactivity, presence of epileptiform activity, and burst suppression with identical bursts (BSIB). MRI was examined for presence of diffusion restriction or fluid-attenuated inversion recovery changes consistent with anoxic brain injury. A prediction model was developed using optimal combination of variables. RESULTS Among 78 patients, 11 (14.1%) recovered at discharge and 6 (7.7%) had good outcome (Cerebral Performance Category < 3) at 3 months. Patients who followed commands were more likely to have pupillary and corneal reflexes, flexion or better motor response, EEG continuity and reactivity, no BSIB, and no anoxic injury on MRI. The combined EEG/MRI variable of continuous background and no anoxic changes on MRI was associated with coma recovery at hospital discharge with sensitivity 91% (95% confidence interval [CI], 0.59-1.00), specificity 99% (95% CI, 0.92-1.00), positive predictive value 91% (95% CI, 0.59-1.00), and negative predictive value 99% (95% CI, 0.92-1.00). CONCLUSIONS EEG and MRI are complementary and identify both good and poor outcome in patients with PAM with high accuracy. An MRI should be considered in patients with myoclonus showing continuous or reactive EEGs.
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Affiliation(s)
- Isabelle Beuchat
- From the Department of Clinical Neuroscience, Neurology Service (I.B., A.O.R.), and Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital and University of Lausanne, Switzerland; Division of Clinical Neurophysiology and Comprehensive Epilepsy Center (A.S., E.J.G.), Department of Neurology, Yale School of Medicine, New Haven, CT; Department of Neurology (E.A., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; and Departments of Radiology (L.H.), Medicine/Cardiology (B.S., D.S.), and Neurology (I.B., K.T., J.W.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Adithya Sivaraju
- From the Department of Clinical Neuroscience, Neurology Service (I.B., A.O.R.), and Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital and University of Lausanne, Switzerland; Division of Clinical Neurophysiology and Comprehensive Epilepsy Center (A.S., E.J.G.), Department of Neurology, Yale School of Medicine, New Haven, CT; Department of Neurology (E.A., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; and Departments of Radiology (L.H.), Medicine/Cardiology (B.S., D.S.), and Neurology (I.B., K.T., J.W.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Edilberto Amorim
- From the Department of Clinical Neuroscience, Neurology Service (I.B., A.O.R.), and Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital and University of Lausanne, Switzerland; Division of Clinical Neurophysiology and Comprehensive Epilepsy Center (A.S., E.J.G.), Department of Neurology, Yale School of Medicine, New Haven, CT; Department of Neurology (E.A., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; and Departments of Radiology (L.H.), Medicine/Cardiology (B.S., D.S.), and Neurology (I.B., K.T., J.W.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Emily J Gilmore
- From the Department of Clinical Neuroscience, Neurology Service (I.B., A.O.R.), and Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital and University of Lausanne, Switzerland; Division of Clinical Neurophysiology and Comprehensive Epilepsy Center (A.S., E.J.G.), Department of Neurology, Yale School of Medicine, New Haven, CT; Department of Neurology (E.A., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; and Departments of Radiology (L.H.), Medicine/Cardiology (B.S., D.S.), and Neurology (I.B., K.T., J.W.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Vincent Dunet
- From the Department of Clinical Neuroscience, Neurology Service (I.B., A.O.R.), and Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital and University of Lausanne, Switzerland; Division of Clinical Neurophysiology and Comprehensive Epilepsy Center (A.S., E.J.G.), Department of Neurology, Yale School of Medicine, New Haven, CT; Department of Neurology (E.A., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; and Departments of Radiology (L.H.), Medicine/Cardiology (B.S., D.S.), and Neurology (I.B., K.T., J.W.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Andrea O Rossetti
- From the Department of Clinical Neuroscience, Neurology Service (I.B., A.O.R.), and Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital and University of Lausanne, Switzerland; Division of Clinical Neurophysiology and Comprehensive Epilepsy Center (A.S., E.J.G.), Department of Neurology, Yale School of Medicine, New Haven, CT; Department of Neurology (E.A., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; and Departments of Radiology (L.H.), Medicine/Cardiology (B.S., D.S.), and Neurology (I.B., K.T., J.W.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - M Brandon Westover
- From the Department of Clinical Neuroscience, Neurology Service (I.B., A.O.R.), and Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital and University of Lausanne, Switzerland; Division of Clinical Neurophysiology and Comprehensive Epilepsy Center (A.S., E.J.G.), Department of Neurology, Yale School of Medicine, New Haven, CT; Department of Neurology (E.A., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; and Departments of Radiology (L.H.), Medicine/Cardiology (B.S., D.S.), and Neurology (I.B., K.T., J.W.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Liangge Hsu
- From the Department of Clinical Neuroscience, Neurology Service (I.B., A.O.R.), and Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital and University of Lausanne, Switzerland; Division of Clinical Neurophysiology and Comprehensive Epilepsy Center (A.S., E.J.G.), Department of Neurology, Yale School of Medicine, New Haven, CT; Department of Neurology (E.A., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; and Departments of Radiology (L.H.), Medicine/Cardiology (B.S., D.S.), and Neurology (I.B., K.T., J.W.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Benjamin M Scirica
- From the Department of Clinical Neuroscience, Neurology Service (I.B., A.O.R.), and Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital and University of Lausanne, Switzerland; Division of Clinical Neurophysiology and Comprehensive Epilepsy Center (A.S., E.J.G.), Department of Neurology, Yale School of Medicine, New Haven, CT; Department of Neurology (E.A., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; and Departments of Radiology (L.H.), Medicine/Cardiology (B.S., D.S.), and Neurology (I.B., K.T., J.W.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Danuzia Silva
- From the Department of Clinical Neuroscience, Neurology Service (I.B., A.O.R.), and Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital and University of Lausanne, Switzerland; Division of Clinical Neurophysiology and Comprehensive Epilepsy Center (A.S., E.J.G.), Department of Neurology, Yale School of Medicine, New Haven, CT; Department of Neurology (E.A., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; and Departments of Radiology (L.H.), Medicine/Cardiology (B.S., D.S.), and Neurology (I.B., K.T., J.W.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Kathleen Tang
- From the Department of Clinical Neuroscience, Neurology Service (I.B., A.O.R.), and Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital and University of Lausanne, Switzerland; Division of Clinical Neurophysiology and Comprehensive Epilepsy Center (A.S., E.J.G.), Department of Neurology, Yale School of Medicine, New Haven, CT; Department of Neurology (E.A., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; and Departments of Radiology (L.H.), Medicine/Cardiology (B.S., D.S.), and Neurology (I.B., K.T., J.W.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Jong Woo Lee
- From the Department of Clinical Neuroscience, Neurology Service (I.B., A.O.R.), and Department of Diagnostic and Interventional Radiology (V.D.), Lausanne University Hospital and University of Lausanne, Switzerland; Division of Clinical Neurophysiology and Comprehensive Epilepsy Center (A.S., E.J.G.), Department of Neurology, Yale School of Medicine, New Haven, CT; Department of Neurology (E.A., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; and Departments of Radiology (L.H.), Medicine/Cardiology (B.S., D.S.), and Neurology (I.B., K.T., J.W.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
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Keijzer HM, Hoedemaekers CWE. Timing is everything: Combining EEG and MRI to predict neurological recovery after cardiac arrest. Resuscitation 2020; 149:240-242. [PMID: 32084570 DOI: 10.1016/j.resuscitation.2020.02.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 02/12/2020] [Indexed: 10/25/2022]
Affiliation(s)
- H M Keijzer
- Department of Neurology, Rijnstate Hospital, Arnhem and Department of Intensive Care Medicine and Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, P.O. Box 9555, 6800 TA Arnhem, The Netherlands.
| | - C W E Hoedemaekers
- Department of Intensive Care Medicine, Radboud University Medical Centre, Nijmegen, P.O. Box 9101, 6500HB Nijmegen, The Netherlands.
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Barth R, Zubler F, Weck A, Haenggi M, Schindler K, Wiest R, Wagner F. Topography of MR lesions correlates with standardized EEG pattern in early comatose survivors after cardiac arrest. Resuscitation 2020; 149:217-224. [PMID: 31982504 DOI: 10.1016/j.resuscitation.2020.01.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 01/01/2020] [Accepted: 01/16/2020] [Indexed: 12/01/2022]
Abstract
AIM Multimodal prognostication in comatose patients after cardiac arrest (CA) is complicated by the fact that different modalities are usually not independent. Here we set out to systematically correlate early EEG and MRI findings. METHODS 89 adult patients from a prospective register who underwent at least one EEG and one MRI in the acute phase after CA were included. The EEGs were characterized using pre-existent standardized categories (highly malignant, malignant, benign). For MRIs, the apparent diffusion coefficient (ADC) was computed in pre-defined regions. We then introduced a novel classification based on the topography of ADC reduction (MR-lesion pattern (MLP) 1: no lesion; MLP 2: purely cortical lesions; MLP 3: involvement of the basal ganglia; MLP 4 involvement of other deep grey matter regions). RESULTS EEG background reactivity and EEG background continuity were strongly associated with a lower MLP value (p < 0.001 and p = 0.003 respectively). The EEG categories highly malignant, malignant and benign were strongly correlated with the MLP values (rho = 0.46, p < 0.001). CONCLUSION The MRI lesions are highly correlated with the EEG pattern. Our results suggest that performing MRI in comatose patients after CA with either highly malignant or with a benign EEG pattern is unlikely to yield additional useful information for prognostication, and should therefore be performed in priority in patients with intermediate EEG patterns ("malignant pattern").
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Affiliation(s)
- Rike Barth
- Sleep-Wake-Epilepsy Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Frederic Zubler
- Sleep-Wake-Epilepsy Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland.
| | - Anja Weck
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland; Department of Intensive Care Medicine, Central Hospital Region Biel/Bienne, Biel/Bienne, Switzerland
| | - Matthias Haenggi
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Kaspar Schindler
- Sleep-Wake-Epilepsy Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Roland Wiest
- Department of Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging (SCAN), Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Franca Wagner
- Department of Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging (SCAN), Inselspital, Bern University Hospital, University of Bern, Switzerland
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Gul SS, Cohen SA, Avery KL, Balakrishnan MP, Balu R, Chowdhury MAB, Crabb D, Huesgen KW, Hwang CW, Maciel CB, Murphy TW, Han F, Becker TK. Cardiac arrest: An interdisciplinary review of the literature from 2018. Resuscitation 2020; 148:66-82. [PMID: 31945428 DOI: 10.1016/j.resuscitation.2019.12.030] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 11/23/2019] [Accepted: 12/15/2019] [Indexed: 12/28/2022]
Abstract
OBJECTIVES The Interdisciplinary Cardiac Arrest Research Review (ICARE) group was formed in 2018 to conduct a systematic annual search of peer-reviewed literature relevant to cardiac arrest (CA). The goals of the review are to illustrate best practices and help reduce knowledge silos by disseminating clinically relevant advances in the field of CA across disciplines. METHODS An electronic search of PubMed using keywords related to CA was conducted. Title and abstracts retrieved by these searches were screened for relevancy, separated by article type (original research or review), and sorted into 7 categories. Screened manuscripts underwent standardized scoring of overall methodological quality and importance. Articles scoring higher than 99 percentiles by category-type were selected for full critique. Systematic differences between editors and reviewer scores were assessed using Wilcoxon signed-rank test. RESULTS A total of 9119 articles were identified on initial search; of these, 1214 were scored after screening for relevance and deduplication, and 80 underwent full critique. Prognostication & Outcomes category comprised 25% and Epidemiology & Public Health 17.5% of fully reviewed articles. There were no differences between editor and reviewer scoring. CONCLUSIONS The total number of articles demonstrates the need for an accessible source summarizing high-quality research findings to serve as a high-yield reference for clinicians and scientists seeking to absorb the ever-growing body of CA-related literature. This may promote further development of the unique and interdisciplinary field of CA medicine.
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Affiliation(s)
- Sarah S Gul
- Department of Surgery, Yale University, New Haven, CT, United States
| | - Scott A Cohen
- Department of Emergency Medicine, University of Florida, Gainesville, FL, United States
| | - K Leslie Avery
- Division of Pediatric Critical Care, Department of Pediatrics, University of Florida, Gainesville, FL, United States
| | | | - Ramani Balu
- Division of Neurocritical Care, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | | | - David Crabb
- Department of Emergency Medicine, University of Florida, Gainesville, FL, United States
| | - Karl W Huesgen
- Department of Emergency Medicine, University of Florida, Gainesville, FL, United States
| | - Charles W Hwang
- Department of Emergency Medicine, University of Florida, Gainesville, FL, United States
| | - Carolina B Maciel
- Division of Neurocritical Care, Department of Neurology, University of Florida, Gainesville, FL, United States; Department of Neurology, Yale University, New Haven, CT, United States
| | - Travis W Murphy
- Department of Emergency Medicine, University of Florida, Gainesville, FL, United States
| | - Francis Han
- Department of Emergency Medicine, University of Florida, Gainesville, FL, United States
| | - Torben K Becker
- Department of Emergency Medicine, University of Florida, Gainesville, FL, United States.
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26
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Wei R, Wang C, He F, Hong L, Zhang J, Bao W, Meng F, Luo B. Prediction of poor outcome after hypoxic-ischemic brain injury by diffusion-weighted imaging: A systematic review and meta-analysis. PLoS One 2019; 14:e0226295. [PMID: 31881032 PMCID: PMC6934311 DOI: 10.1371/journal.pone.0226295] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2019] [Accepted: 11/24/2019] [Indexed: 01/16/2023] Open
Abstract
Accurate prediction of the neurological outcome following hypoxic–ischemic brain injury (HIBI) remains difficult. Diffusion-weighted imaging (DWI) can detect acute and subacute brain abnormalities following global cerebral hypoxia. Therefore, DWI can be used to predict the outcomes of HIBI. To this end, we searched the PubMed, EMBASE, and Cochrane Library databases for studies that examine the diagnostic accuracy of DWI in predicting HIBI outcomes in adult patients between January1995 and September 2019. Next, we conducted a comprehensive meta-analysis using the Meta-DiSc and several complementary techniques. Following the application of inclusion and exclusion criteria, a total of 28 studies were included with 98 data subsets. The overall sensitivity and specificity, with 95% confidence interval, were 0.613(0.599–0.628) and 0.958(0.947–0.967), respectively, and the area under the curve was 0.9090. Significant heterogeneity among the included studies and a threshold effect were observed (p<0.001). Different positive indices were the major sources for the heterogeneity, followed by the anatomical region examined, both of which significantly affected the prognostic accuracy. In conclusion, we demonstrated that DWI can be an instrumental modality in predicting the outcome of HIBI with good prognostic accuracy. However, the lack of clear and generally accepted positive indices limits its clinical application. Therefore, using more reliable positive indices and combining DWI with other clinical predictors may improve the diagnostic accuracy of HIBI.
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Affiliation(s)
- Ruili Wei
- Department of Neurology, Brain Medical Centre, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chaonan Wang
- Department of Geriatrics, Shulan (Hangzhou) Hospital, Hangzhou, China
| | - Fangping He
- Department of Neurology, Brain Medical Centre, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lirong Hong
- Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Jie Zhang
- Department of Rehabilitation, Hangzhou Hospital of Zhejiang CAPR, Hangzhou, China
| | - Wangxiao Bao
- Department of Neurology, Brain Medical Centre, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fangxia Meng
- Department of Neurology, Brain Medical Centre, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Benyan Luo
- Department of Neurology, Brain Medical Centre, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- * E-mail:
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27
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Chang JJ, Tsivgoulis G, Goyal N, Alsherbini K, Schuring C, Shrestha R, Yankovich A, Metter JE, Sareen S, Elijovich L, Malkoff MD, Murillo L, Kadaria D, Alexandrov AV, Sodhi A. Prognostication via early computed tomography head in patients treated with targeted temperature management after cardiac arrest. J Neurol Sci 2019; 406:116437. [PMID: 31521958 DOI: 10.1016/j.jns.2019.116437] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 08/10/2019] [Accepted: 08/27/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND We evaluated computed tomography head (CTH) imaging obtained prior to targeted temperature management (TTM) in patients after cardiac arrest, and its role in prognostication. METHODS In this retrospective cohort study in a tertiary-care hospital, 341 adults presenting with out-of-hospital cardiac arrest received a CTH prior to TTM. Associations between outcomes and neuroimaging variables were evaluated with Chi-square analysis for significant associations that yielded a composite neuroimaging score-Tennessee Early Neuroimaging Score (TENS). Univariable and multivariable logistic regression analysis including TENS as an independent variable and the four outcome dependent variables were analyzed. RESULTS Four of the neuroimaging variables-sulcal effacement, partial gray-white matter effacement, total gray-white matter effacement, deep nuclei effacement-had significant associations with each of the four outcome variables and yielded TENS. In multivariable logistic regression models adjusted for potential confounders, TENS was associated with poor discharge CPC (OR 2.15, 95%CI 1.16-3.98, p = .015), poor disposition (OR 2.62, 95%CI 1.37-5.02, p = .004), in-hospital mortality (OR 1.99, 95%CI 1.09-3.62, p = .024), and ICU mortality (OR 1.89, 95%CI 1.12-3.20, p = .018). CONCLUSION Imaging prior to TTM may help identify post-cardiac arrest patients with severe anoxic brain injury and poor outcomes.
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Affiliation(s)
- Jason J Chang
- Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA; Department of Critical Care Medicine, MedStar Washington Hospital Center, Washington, DC, USA.
| | - Georgios Tsivgoulis
- Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA; Second Department of Neurology, "Attikon University Hospital", National & Kapodistrian University of Athens, School of Medicine, Athens, Greece
| | - Nitin Goyal
- Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Khalid Alsherbini
- Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA; Department of Neurosurgery, Semmes-Murphey Clinic, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Craig Schuring
- Division of Pulmonary and Critical Care Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Rabin Shrestha
- Division of Pulmonary and Critical Care Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Andrei Yankovich
- Division of Pulmonary and Critical Care Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Jeffrey E Metter
- Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Srishti Sareen
- Division of Pulmonary and Critical Care Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Lucas Elijovich
- Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA; Department of Neurosurgery, Semmes-Murphey Clinic, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Marc D Malkoff
- Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA; Department of Neurosurgery, Semmes-Murphey Clinic, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Luis Murillo
- Division of Pulmonary and Critical Care Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Dipen Kadaria
- Division of Pulmonary and Critical Care Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Andrei V Alexandrov
- Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Amik Sodhi
- Division of Pulmonary and Critical Care Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
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28
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Assessing brain injury after cardiac arrest, towards a quantitative approach. Curr Opin Crit Care 2019; 25:211-217. [DOI: 10.1097/mcc.0000000000000611] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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29
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Nguyen PL, Alreshaid L, Poblete RA, Konye G, Marehbian J, Sung G. Targeted Temperature Management and Multimodality Monitoring of Comatose Patients After Cardiac Arrest. Front Neurol 2018; 9:768. [PMID: 30254606 PMCID: PMC6141756 DOI: 10.3389/fneur.2018.00768] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 08/24/2018] [Indexed: 01/14/2023] Open
Abstract
Out-of-hospital cardiac arrest (CA) remains a leading cause of sudden morbidity and mortality; however, outcomes have continued to improve in the era of targeted temperature management (TTM). In this review, we highlight the clinical use of TTM, and provide an updated summary of multimodality monitoring possible in a modern ICU. TTM is neuroprotective for survivors of CA by inhibiting multiple pathophysiologic processes caused by anoxic brain injury, with a final common pathway of neuronal death. Current guidelines recommend the use of TTM for out-of-hospital CA survivors who present with a shockable rhythm. Further studies are being completed to determine the optimal timing, depth and duration of hypothermia to optimize patient outcomes. Although a multidisciplinary approach is necessary in the CA population, neurologists and neurointensivists are central in selecting TTM candidates and guiding patient care and prognostic evaluation. Established prognostic tools include clinal exam, SSEP, EEG and MR imaging, while functional MRI and invasive monitoring is not validated to improve outcomes in CA or aid in prognosis. We recommend that an evidence-based TTM and prognostication algorithm be locally implemented, based on each institution's resources and limitations. Given the high incidence of CA and difficulty in predicting outcomes, further study is urgently needed to determine the utility of more recent multimodality devices and studies.
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Affiliation(s)
- Peggy L Nguyen
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Laith Alreshaid
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Roy A Poblete
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Geoffrey Konye
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Jonathan Marehbian
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Gene Sung
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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