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Endisch C, Millard K, Preuß S, Stenzel W, Ploner CJ, Storm C, Nee J, Leithner C. Histopathological patterns of hypoxic-ischemic encephalopathy after cardiac arrest: A retrospective brain autopsy study of 319 patients. Resuscitation 2025:110608. [PMID: 40246166 DOI: 10.1016/j.resuscitation.2025.110608] [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: 12/12/2024] [Revised: 04/06/2025] [Accepted: 04/07/2025] [Indexed: 04/19/2025]
Abstract
PURPOSE Understanding the pathophysiology of hypoxic-ischemic encephalopathy (HIE) provides important knowledge for the interpretation of neuroprognostic investigations after cardiac arrest (CA). One important aspect are the patterns of regional severity of selective neuronal death within the brain. METHODS In a monocentric, retrospective study, we included CA patients with initially successful resuscitation, who had received brain autopsies after death. We quantified selective eosinophilic neuronal death (SEND) in cerebral neocortex, hippocampus, basal ganglia, cerebellum, and brainstem. Using a previously established classification, we dichotomized HIE severity in SEND 0-1 (<30%, reflecting no or mild HIE) versus SEND 2-4 (≥30%, reflecting moderate to severe HIE). We analyzed histopathological HIE patterns and analyzed inter-regional and inter-neocortical correlation of SEND. RESULTS Of 319 patients, the mean SEND was 1.8 in hippocampus, 1.4 in neocortex, and 0.9 in brainstem. Typical histopathological HIE patterns were: (I) No or mild SEND in all brain regions, (II) predominant SEND in hippocampus with no or mild SEND in other brain regions, (III) severe SEND in neocortex, but not in brainstem, and (IV) severe SEND in the brainstem with neocortical HIE. In 7(9.7%) of 72 patients with histopathology from two different neocortical regions, the SEND differed by more than 30%. Among 154 patients with a SEND greater than 30% in at least one brain region, 14(9.1%) had predominant SEND in cerebellum, and 4(2.6%) predominant SEND in basal ganglia. CONCLUSIONS CA causes typical histopathological HIE patterns, with the hippocampus being more susceptible to SEND, than the cerebral neocortex, and the brainstem being the most resistant brain region. The neocortical distribution of SEND is mostly homogeneous; however, a relevant subgroup shows substantial neocortical HIE heterogeneity. Further studies are required to provide a more granular histopathological analysis of infrequent HIE patterns and their implications for neuroprognostication.
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Affiliation(s)
- Christian Endisch
- Department of Neurology, AG Emergency and Critical Care Neurology, Campus Virchow Klinikum, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.
| | - Katharina Millard
- Department of Neurology, AG Emergency and Critical Care Neurology, Campus Virchow Klinikum, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Sandra Preuß
- Department of Neurology, AG Emergency and Critical Care Neurology, Campus Virchow Klinikum, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany; Department of Cardiology and Angiology, Charité Campus Mitte, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Werner Stenzel
- Department of Neuropathology, Charité Campus Mitte, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Christoph J Ploner
- Department of Neurology, AG Emergency and Critical Care Neurology, Campus Virchow Klinikum, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Christian Storm
- Department of Nephrology and Intensive Care Medicine, Campus Virchow Klinikum, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Jens Nee
- Department of Nephrology and Intensive Care Medicine, Campus Virchow Klinikum, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Christoph Leithner
- Department of Neurology, AG Emergency and Critical Care Neurology, Campus Virchow Klinikum, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
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2
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Van Roy S, Hsu L, Ho J, Scirica BM, Fischer D, Snider SB, Lee JW. Quantitative and Radiological Assessment of Post-cardiac-Arrest Comatose Patients with Diffusion-Weighted Magnetic Resonance Imaging. Neurocrit Care 2025; 42:541-550. [PMID: 39164537 DOI: 10.1007/s12028-024-02087-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 07/29/2024] [Indexed: 08/22/2024]
Abstract
BACKGROUND Although magnetic resonance imaging, particularly diffusion-weighted imaging, has increasingly been used as part of a multimodal approach to prognostication in patients who are comatose after cardiac arrest, the performance of quantitative analysis of apparent diffusion coefficient (ADC) maps, as compared to standard radiologist impression, has not been well characterized. This retrospective study evaluated quantitative ADC analysis to the identification of anoxic brain injury by diffusion abnormalities on standard clinical magnetic resonance imaging reports. METHODS The cohort included 204 previously described comatose patients after cardiac arrest. Clinical outcome was assessed by (1) 3-6 month post-cardiac-arrest cerebral performance category and (2) coma recovery to following commands. Radiological evaluation was obtained from clinical reports and characterized as diffuse, cortex only, deep gray matter structures only, or no anoxic injury. Quantitative analyses of ADC maps were obtained in specific regions of interest (ROIs), whole cortex, and whole brain. A subgroup analysis of 172 was performed after eliminating images with artifacts and preexisting lesions. RESULTS Radiological assessment outperformed quantitative assessment over all evaluated regions (area under the curve [AUC] 0.80 for radiological interpretation and 0.70 for the occipital region, the best performing ROI, p = 0.011); agreement was substantial for all regions. Radiological assessment still outperformed quantitative analysis in the subgroup analysis, though by smaller margins and with substantial to near-perfect agreement. When assessing for coma recovery only, the difference was no longer significant (AUC 0.83 vs. 0.81 for the occipital region, p = 0.70). CONCLUSIONS Although quantitative analysis eliminates interrater differences in the interpretation of abnormal diffusion imaging and avoids bias from other prediction modalities, clinical radiologist interpretation has a higher predictive value for outcome. Agreement between radiological and quantitative analysis improved when using high-quality scans and when assessing for coma recovery using following commands. Quantitative assessment may thus be more subject to variability in both clinical management and scan quality than radiological assessment.
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Affiliation(s)
- Sam Van Roy
- Division of EEG and Epilepsy, Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd, Boston, MA, 02115, USA
| | - Liangge Hsu
- Division of Neuroradiology, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Joseph Ho
- Division of EEG and Epilepsy, Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd, Boston, MA, 02115, USA
| | - Benjamin M Scirica
- Division of Cardiology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - David Fischer
- Department of Neurology, Hospital of Pennsylvania, Philadelphia, PA, USA
| | - Samuel B Snider
- Division of Neurocritical Care, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Jong Woo Lee
- Division of EEG and Epilepsy, Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd, Boston, MA, 02115, USA.
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3
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Eastwood GM, Bailey M, Nichol AD, Dankiewicz J, Nielsen N, Parke R, Cronberg T, Olasveengen T, Grejs AM, Iten M, Haenggi M, McGuigan P, Wagner F, Moseby-Knappe M, Lang M, Bellomo R. Magnetic resonance imaging in comatose adults resuscitated after out-of-hospital cardiac arrest: A posthoc study of the Targeted Therapeutic Mild Hypercapnia after Resuscitated Cardiac Arrest trial. Aust Crit Care 2025; 38:101130. [PMID: 39516151 DOI: 10.1016/j.aucc.2024.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2024] [Accepted: 09/30/2024] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND Neuroimaging with magnetic resonance imaging (MRI) may assist clinicians in evaluating brain injury and optimising care in comatose adults resuscitated after out-of-hospital cardiac arrest (OHCA). However, contemporary international data on its use are lacking. AIM The primary aim was to compare the patient characteristics, early postresuscitation care, and neurological outcomes of patients according to MRI use. METHODS We performed a posthoc analysis of the Targeted Therapeutic Mild Hypercapnia after Resuscitated Cardiac Arrest (TAME) trial, a multinational randomised trial comparing targeted mild hypercapnia or normocapnia in comatose adults after OHCA. RESULTS After exclusions, 1639 patients enrolled in the TAME trial were analysed. Of these, 149 (9%) had an MRI. Compared to non-MRI patients, MRI patients were younger (58.9 versus 61.7 years, p: 0.02), had a longer median time from OHCA to return of spontaneous circulation (30 versus 25 min, p < 0.0001), and had a higher average arterial lactate level (8.78 versus 6.74 mmol/L, p < 0.0001) on admission to hospital. MRI patients were more likely to receive additional advanced diagnostic assessments during intensive care unit admission (p < 0.0001). At 6 months, 23 of 140 patients (16.4%) in the MRI group had a favourable neurological outcome, compared with 659 of 1399 patients (47.1%) in the no-MRI group (p < 0.001). On multivariable modelling, country of enrolment was the dominating predictor in the likelihood of an MRI being performed. CONCLUSIONS In the TAME trial, 9% of patients had an MRI during their intensive care unit admission. Among these patients, only 16% had a favourable neurological outcome at 6 months.
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Affiliation(s)
- Glenn M Eastwood
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia; Department of Intensive Care, Austin Hospital, Heidelberg, Australia.
| | - Michael Bailey
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia
| | - Alistair D Nichol
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia; University College Dublin Clinical Research Centre at St Vincents University Hospital, Dublin, Ireland
| | - Josef Dankiewicz
- Department of Clinical Sciences Lund, Section of Cardiology, Skåne University Hospital and Lund University, Sweden
| | - Niklas Nielsen
- Department of Clinical Sciences Lund, Anesthesiology and Intensive Care, Lund University, Sweden; Department of Anesthesiology and Intensive Care, Helsingborg Hospital, Helsingborg, Sweden
| | - Rachael Parke
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia; Cardiothoracic and Vascular Intensive Care Unit, Auckland City Hospital, Te Toka Tumai Auckland, Auckland, New Zealand; School of Nursing, The University of Auckland, Auckland, New Zealand; Medical Research Institute of New Zealand, Wellington, New Zealand
| | - Tobias Cronberg
- Head of Department of Neurology, Rehabilitation Medicine, Memory Disorders and Geriatrics, Sweden; Skåne University Hospital, Sweden
| | - Theresa Olasveengen
- Department of Anaesthesia and Intensive Care, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway
| | - Anders M Grejs
- Department of Intensive Care Medicine, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Manuela Iten
- Department of Intensive Care Medicine, Inselspital University Hospital Bern, Bern, Switzerland
| | - Matthias Haenggi
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Peter McGuigan
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, United Kingdom
| | - Franca Wagner
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital University Hospital Bern, and University of Bern, Bern, Switzerland
| | - Marion Moseby-Knappe
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden; Department of Neurology and Rehabilitation, Skåne University Hospital, Lund, Sweden
| | - Margareta Lang
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden; Department of Radiology, Helsingborg Hospital, Helsingborg, Sweden
| | - Rinaldo Bellomo
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia; Department of Intensive Care, Austin Hospital, Heidelberg, Australia; Centre for Integrated Critical Care, The University of Melbourne, Melbourne, Australia
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Diamanti S, Pasini F, Capraro C, Patassini M, Bianchi E, Pozzi M, Normanno M, Coppo A, Remida P, Avalli L, Ferrarese C, Foti G, Beretta S. Prognostic Value of Signal Abnormalities on Brain MRI in Post-Anoxic Super-Refractory Status Epilepticus: A Single-Center Retrospective Study. Eur J Neurol 2025; 32:e70045. [PMID: 39817609 PMCID: PMC11736634 DOI: 10.1111/ene.70045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 10/23/2024] [Accepted: 12/27/2024] [Indexed: 01/18/2025]
Abstract
BACKGROUND Epileptiform activity, including status epilepticus (SE), occurs in up to one-third of comatose survivors of cardiac arrest and may predict poor outcome. The relationship between SE and hypoxic-ischemic brain injury (HIBI) is not established. METHODS This is a single-center retrospective study on consecutive patients with post-anoxic super-refractory SE. HIBI was graded as non-widespread (group 1) or widespread (group 2) by qualitative analysis of DWI/ADC and T2w-FLAIR. Between-group differences in the rate of poor neurological outcome at 6 months (primary outcome), SE resolution and consciousness recovery before discharge, and mortality at 6 months (secondary outcomes) were investigated. RESULTS From January 2011 to February 2023, 40 patients were included. HIBI was widespread in 45% of patients and non-widespread in 55%. The rate of poor neurological outcome at 6 months was 27% in group 1 and 83% in group 2 (OR 12.8, CI 95% [2.5-64.3], p = 0.002). The rate of consciousness recovery before discharge was 73% in group 1 versus 22% in group 2 (OR 8.8, CI 95% [1.9-40.3], p = 0.005). SE resolved in 95% of patients in group 1 versus 67% in group 2 (OR 10.5, CI 95% [1.1-97.9], p = 0.039). Mortality rate at 6 months was 27% in group 1 versus 50% in group 2 (OR 0.4, CI 95% [0.1-1.9], p = 0.303). CONCLUSION Patients with widespread HIBI had higher odds of poor outcome at 6 months, lower probability of SE resolution and of consciousness recovery before discharge compared to those with non-widespread HIBI. Mortality at 6 months did not differ significantly between the two groups.
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Affiliation(s)
- Susanna Diamanti
- Epilepsy Center, Department of NeurologyFondazione IRCCS San Gerardo Dei TintoriMonzaItaly
| | - Francesco Pasini
- Epilepsy Center, Department of NeurologyFondazione IRCCS San Gerardo Dei TintoriMonzaItaly
- Department of Medicine and SurgeryUniversity of Milano‐BicoccaMilanoItaly
| | - Cristina Capraro
- Neuroradiology UnitFondazione IRCCS San Gerardo Dei TintoriMonzaItaly
| | - Mirko Patassini
- Neuroradiology UnitFondazione IRCCS San Gerardo Dei TintoriMonzaItaly
| | - Elisa Bianchi
- IRCCS Mario Negri Institute for Pharmacological ResearchMilanoItaly
| | - Matteo Pozzi
- Department of Intensive CareFondazione IRCCS San Gerardo Dei TintoriMonzaItaly
| | - Marco Normanno
- Department of Intensive CareFondazione IRCCS San Gerardo Dei TintoriMonzaItaly
| | - Anna Coppo
- Department of Intensive CareFondazione IRCCS San Gerardo Dei TintoriMonzaItaly
| | - Paolo Remida
- Neuroradiology UnitFondazione IRCCS San Gerardo Dei TintoriMonzaItaly
| | - Leonello Avalli
- Department of Intensive CareFondazione IRCCS San Gerardo Dei TintoriMonzaItaly
| | - Carlo Ferrarese
- Epilepsy Center, Department of NeurologyFondazione IRCCS San Gerardo Dei TintoriMonzaItaly
- Department of Medicine and SurgeryUniversity of Milano‐BicoccaMilanoItaly
- Milan Center for NeuroscienceMilanoItaly
| | - Giuseppe Foti
- Department of Medicine and SurgeryUniversity of Milano‐BicoccaMilanoItaly
- Department of Intensive CareFondazione IRCCS San Gerardo Dei TintoriMonzaItaly
| | - Simone Beretta
- Epilepsy Center, Department of NeurologyFondazione IRCCS San Gerardo Dei TintoriMonzaItaly
- Department of Medicine and SurgeryUniversity of Milano‐BicoccaMilanoItaly
- Milan Center for NeuroscienceMilanoItaly
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5
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Iten M, Moser A, Wagner F, Haenggi M. Performance of the MRI lesion pattern score in predicting neurological outcome after out of hospital cardiac arrest: a retrospective cohort analysis. Crit Care 2024; 28:215. [PMID: 38956665 PMCID: PMC11220945 DOI: 10.1186/s13054-024-05007-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 06/27/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Despite advances in resuscitation practice, patient survival following cardiac arrest remains poor. The utilization of MRI in neurological outcome prognostication post-cardiac arrest is growing and various classifications has been proposed; however a consensus has yet to be established. MRI, though valuable, is resource-intensive, time-consuming, costly, and not universally available. This study aims to validate a MRI lesion pattern score in a cohort of out of hospital cardiac arrest patients at a tertiary referral hospital in Switzerland. METHODS This cohort study spanned twelve months from February 2021 to January 2022, encompassing all unconscious patients aged ≥ 18 years who experienced out-of-hospital cardiac arrest of any cause and were admitted to the intensive care unit (ICU) at Inselspital, University Hospital Bern, Switzerland. We included patients who underwent the neuroprognostication process, assessing the performance and validation of a MRI scoring system. RESULTS Over the twelve-month period, 137 patients were admitted to the ICU, with 52 entering the neuroprognostication process and 47 undergoing MRI analysis. Among the 35 MRIs indicating severe hypoxic brain injury, 33 patients (94%) experienced an unfavourable outcome (UO), while ten (83%) of the twelve patients with no or minimal MRI lesions had a favourable outcome. This yielded a sensitivity of 0.94 and specificity of 0.83 for predicting UO with the proposed MRI scoring system. The positive and negative likelihood ratios were 5.53 and 0.07, respectively, resulting in an accuracy of 91.49%. CONCLUSION We demonstrated the effectiveness of the MLP scoring scheme in predicting neurological outcome in patients following cardiac arrest. However, to ensure a comprehensive neuroprognostication, MRI results need to be combined with other assessments. While neuroimaging is a promising objective tool for neuroprognostication, given the absence of sedation-related confounders-compared to electroencephalogram (EEG) and clinical examination-the current lack of a validated scoring system necessitates further studies. Incorporating standardized MRI techniques and grading systems is crucial for advancing the reliability of neuroimaging for neuroprognostication. TRIAL REGISTRATION Registry of all Projects in Switzerland (RAPS) 2020-01761.
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Affiliation(s)
- Manuela Iten
- Department of Intensive Care Medicine, Inselspital, University Hospital Bern, Bern, Switzerland.
| | - Antonia Moser
- Department of Intensive Care Medicine, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Franca Wagner
- University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Matthias Haenggi
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
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Hakim A, Branca M, Kurmann C, Wagner B, Iten M, Hänggi M, Wagner F. CT brain perfusion patterns and clinical outcome after successful cardiopulmonary resuscitation: A pilot study. Resuscitation 2024; 200:110216. [PMID: 38626861 DOI: 10.1016/j.resuscitation.2024.110216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 04/07/2024] [Accepted: 04/08/2024] [Indexed: 06/19/2024]
Abstract
AIM CT perfusion is a valuable tool for evaluating cerebrovascular diseases, but its role in patients with hypoxic ischaemic encephalopathy is unclear. This study aimed to investigate 1) the patterns of cerebral perfusion changes that may occur early on after successful resuscitation, and 2) their correlation with clinical outcome to explore their value for predicting outcome. METHODS We conducted a retrospective analysis of perfusion maps from patients who underwent CT brain perfusion within 12 h following successful resuscitation. We classified the perfusion changes into distinct patterns. According to the cerebral performance category (CPC) score clinical outcome was categorised as favourable (CPC 1-2), or unfavourable (CPC 3-5). RESULTS A total of 87 patients were included of whom 33 had a favourable outcome (60.6% male, mean age 60 ± 16 years), whereas 54 exhibited an unfavourable outcome (59.3% male, mean age 60 ± 19 years). Of the patients in the favourable outcome group, 30.3% showed no characteristic perfusion changes, in contrast to the unfavourable outcome group where all patients exhibit changes in perfusion. Eighteen perfusion patterns were identified. The most significant patterns for prediction of unfavourable outcome in terms of their high specificity and frequency were hypoperfusion of the brainstem as well as coexisting hypoperfusion of the brainstem and thalamus. CONCLUSION This pilot study identified various perfusion patterns in patients after resuscitation, indicative of circulatory changes associated with post-cardiac-arrest brain injury. After validation, certain patterns could potentially be used in conjunction with other prognostic markers for stratifying patients and adjusting personalized treatment following cardiopulmonary resuscitation. Normal brain perfusion within 12 h after resuscitation is predictive of favourable outcome with high specificity.
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Affiliation(s)
- Arsany Hakim
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital Bern University Hospital, and University of Bern, Bern, Switzerland.
| | | | - Christoph Kurmann
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Benedikt Wagner
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Manuela Iten
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Matthias Hänggi
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Franca Wagner
- University Institute of Diagnostic and Interventional Neuroradiology, Inselspital Bern University Hospital, and University of Bern, Bern, Switzerland
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7
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Bach AM, Kirschen MP, Fung FW, Abend NS, Ampah S, Mondal A, Huh JW, Chen SSL, Yuan I, Graham K, Berman JI, Vossough A, Topjian A. Association of EEG Background With Diffusion-Weighted Magnetic Resonance Neuroimaging and Short-Term Outcomes After Pediatric Cardiac Arrest. Neurology 2024; 102:e209134. [PMID: 38350044 PMCID: PMC11384654 DOI: 10.1212/wnl.0000000000209134] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/16/2023] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND AND OBJECTIVES EEG and MRI features are independently associated with pediatric cardiac arrest (CA) outcomes, but it is unclear whether their combination improves outcome prediction. We aimed to assess the association of early EEG background category with MRI ischemia after pediatric CA and determine whether addition of MRI ischemia to EEG background features and clinical variables improves short-term outcome prediction. METHODS This was a single-center retrospective cohort study of pediatric CA with EEG initiated ≤24 hours and MRI obtained ≤7 days of return of spontaneous circulation. Initial EEG background was categorized as normal, slow/disorganized, discontinuous/burst-suppression, or attenuated-featureless. MRI ischemia was defined as percentage of brain tissue with apparent diffusion coefficient (ADC) <650 × 10-6 mm2/s and categorized as high (≥10%) or low (<10%). Outcomes were mortality and unfavorable neurologic outcome (Pediatric Cerebral Performance Category increase ≥1 from baseline resulting in ICU discharge score ≥3). The Kruskal-Wallis test evaluated the association of EEG with MRI. Area under the receiver operating characteristic (AUROC) curve evaluated predictive accuracy. Logistic regression and likelihood ratio tests assessed multivariable outcome prediction. RESULTS We evaluated 90 individuals. EEG background was normal in 16 (18%), slow/disorganized in 42 (47%), discontinuous/burst-suppressed in 12 (13%), and attenuated-featureless in 20 (22%) individuals. The median percentage of MRI ischemia was 5% (interquartile range 1-18); 32 (36%) individuals had high MRI ischemia burden. Twenty-eight (31%) individuals died, and 58 (64%) had unfavorable neurologic outcome. Worse EEG background category was associated with more MRI ischemia (p < 0.001). The combination of EEG background and MRI ischemia burden had higher predictive accuracy than EEG alone (AUROC: mortality: 0.92 vs 0.87, p = 0.03) or MRI alone (AUROC: mortality: 0.92 vs 0.84, p = 0.02; unfavorable: 0.83 vs 0.73, p < 0.01). Addition of percentage of MRI ischemia to clinical variables and EEG background category improved prediction for mortality (χ2 = 19.1, p < 0.001) and unfavorable neurologic outcome (χ2 = 4.8, p = 0.03) and achieved high predictive accuracy (AUROC: mortality: 0.97; unfavorable: 0.92). DISCUSSION Early EEG background category was associated with MRI ischemia after pediatric CA. Combining EEG and MRI data yielded higher outcome predictive accuracy than either modality alone. The addition of MRI ischemia to clinical variables and EEG background improved short-term outcome prediction.
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Affiliation(s)
- Ashley M Bach
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
| | - Matthew P Kirschen
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
| | - France W Fung
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
| | - Nicholas S Abend
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
| | - Steve Ampah
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
| | - Antara Mondal
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
| | - Jimmy W Huh
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
| | - Shih-Shan L Chen
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
| | - Ian Yuan
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
| | - Kathryn Graham
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
| | - Jeffrey I Berman
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
| | - Arastoo Vossough
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
| | - Alexis Topjian
- From the Department of Neurology (A.M.B., M.P.K., F.W.F., N.S.A.), Departments of Anesthesia and Critical Care Medicine (M.P.K., N.S.A., J.W.H., I.Y., K.G., A.T.), Department of Pediatrics (M.P.K., N.S.A., J.W.H., A.T.), Department of Biomedical and Health Informatics (S.A., A.M.), Department of Neurosurgery (S.-S.L.C.), and Department of Radiology (J.I.B., A.V.), Children's Hospital of Philadelphia, PA
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8
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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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9
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Keijzer HM, Duering M, Pasternak O, Meijer FJA, Verhulst MMLH, Tonino BAR, Blans MJ, Hoedemaekers CWE, Klijn CJM, Hofmeijer J. 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: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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Affiliation(s)
- Hanneke M Keijzer
- Department of Neurology, Rijnstate Hospital, P.O. box 9555, 6800 TA, Arnhem, The Netherlands.
- Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, 6500 HC, Nijmegen, The Netherlands.
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital LMU, 81377, Munich, Munich, Germany
- Medical Image Analysis Centre (MIAC AG), Basel and qbig, Department of Biomedical Engineering, University of Basel, CH-4051, Basel, Switzerland
| | - Ofer Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Frederick J A Meijer
- Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, 6500 HC, Nijmegen, The Netherlands
| | - Marlous M L H Verhulst
- Department of Neurology, Rijnstate Hospital, P.O. box 9555, 6800 TA, Arnhem, The Netherlands
- Department of Clinical Neurophysiology, Faculty of Science and Technology, University of Twente, 7522 NB, Enschede, The Netherlands
| | - Bart A R Tonino
- Department of Radiology, Rijnstate Hospital, 6800 TA, Arnhem, The Netherlands
| | - Michiel J Blans
- Department of Intensive Care Medicine, 6800 TA, Rijnstate Hospital, Arnhem, The Netherlands
| | - Cornelia W E Hoedemaekers
- Department of Intensive Care Medicine, Radboud University Medical Centre, 6500 HC, Nijmegen, 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
| | - Jeannette Hofmeijer
- Department of Neurology, Rijnstate Hospital, P.O. box 9555, 6800 TA, Arnhem, The Netherlands
- Department of Clinical Neurophysiology, Faculty of Science and Technology, University of Twente, 7522 NB, Enschede, The Netherlands
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10
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Hakim A, Wagner F. Preferential Perfusion to the Posterior Circulation in Hypoxia. Ann Neurol 2023; 93:417-418. [PMID: 36282431 DOI: 10.1002/ana.26539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 01/31/2023]
Affiliation(s)
- Arsany Hakim
- University Institute of Diagnostic and Interventional Neuroradiology, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland
| | - Franca Wagner
- University Institute of Diagnostic and Interventional Neuroradiology, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland
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11
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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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12
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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: 14] [Impact Index Per Article: 4.7] [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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13
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Kim YM, Jeung KW, Kim WY, Park YS, Oh JS, You YH, Lee DH, Chae MK, Jeong YJ, Kim MC, Ha EJ, Hwang KJ, Kim WS, Lee JM, Cha KC, Chung SP, Park JD, Kim HS, Lee MJ, Na SH, Kim ARE, Hwang SO. 2020 Korean Guidelines for Cardiopulmonary Resuscitation. Part 5. Post-cardiac arrest care. Clin Exp Emerg Med 2021; 8:S41-S64. [PMID: 34034449 PMCID: PMC8171174 DOI: 10.15441/ceem.21.025] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 03/07/2021] [Accepted: 03/19/2021] [Indexed: 12/20/2022] Open
Affiliation(s)
- Young-Min Kim
- Department of Emergency Medicine, The Catholic University of Korea College of Medicine, Seoul, Korea
| | - Kyung Woon Jeung
- Department of Emergency Medicine, Chonnam National University College of Medicine, Gwangju, Korea
| | - Won Young Kim
- Department of Emergency Medicine, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Yoo Seok Park
- Department of Emergency Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Joo Suk Oh
- Department of Emergency Medicine, The Catholic University of Korea College of Medicine, Seoul, Korea
| | - Yeon Ho You
- Department of Emergency Medicine, Chungnam National University College of Medicine, Daejeon, Korea
| | - Dong Hoon Lee
- Department of Emergency Medicine, Chung-Ang University College of Medicine, Seoul, Korea
| | - Minjung Kathy Chae
- Department of Emergency Medicine, Ajou University College of Medicine, Suwon, Korea
| | - Yoo Jin Jeong
- Department of Emergency Medicine, Chonnam National University College of Medicine, Gwangju, Korea
| | - Min Chul Kim
- Department of Internal Medicine, Chonnam National University College of Medicine, Gwangju, Korea
| | - Eun Jin Ha
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Korea
| | - Kyoung Jin Hwang
- Department of Neurology, Kyung Hee University College of Medicine, Seoul, Korea
| | - Won-Seok Kim
- Department of Rehabilitation Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jae Myung Lee
- Department of General Surgery, Korea University College of Medicine, Seoul, Korea
| | - Kyoung-Chul Cha
- Department of Emergency Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Sung Phil Chung
- Department of Emergency Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - June Dong Park
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea
| | - Han-Suk Kim
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea
| | - Mi Jin Lee
- Department of Emergency Medicine, Kyoungbook University College of Medicine, Daegu, Korea
| | - Sang-Hoon Na
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Ai-Rhan Ellen Kim
- Department of Pediatrics, Ulsan University College of Medicine, Seoul, Korea
| | - Sung Oh Hwang
- Department of Emergency Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - on behalf of the Steering Committee of 2020 Korean Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care
- Department of Emergency Medicine, The Catholic University of Korea College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Chonnam National University College of Medicine, Gwangju, Korea
- Department of Emergency Medicine, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Yonsei University College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Chungnam National University College of Medicine, Daejeon, Korea
- Department of Emergency Medicine, Chung-Ang University College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Ajou University College of Medicine, Suwon, Korea
- Department of Internal Medicine, Chonnam National University College of Medicine, Gwangju, Korea
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Korea
- Department of Neurology, Kyung Hee University College of Medicine, Seoul, Korea
- Department of Rehabilitation Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
- Department of General Surgery, Korea University College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Kyoungbook University College of Medicine, Daegu, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Pediatrics, Ulsan University College of Medicine, Seoul, Korea
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14
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Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2020; 24:680. [PMID: 33287874 PMCID: PMC7720582 DOI: 10.1186/s13054-020-03407-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 11/24/2020] [Indexed: 01/08/2023]
Abstract
BACKGROUND Early prognostication in patients with acute consciousness impairment is a challenging but essential task. Current prognostic guidelines vary with the underlying etiology. In particular, electroencephalography (EEG) is the most important paraclinical examination tool in patients with hypoxic ischemic encephalopathy (HIE), whereas it is not routinely used for outcome prediction in patients with traumatic brain injury (TBI). METHOD Data from 364 critically ill patients with acute consciousness impairment (GCS ≤ 11 or FOUR ≤ 12) of various etiologies and without recent signs of seizures from a prospective randomized trial were retrospectively analyzed. Random forest classifiers were trained using 8 visual EEG features-first alone, then in combination with clinical features-to predict survival at 6 months or favorable functional outcome (defined as cerebral performance category 1-2). RESULTS The area under the ROC curve was 0.812 for predicting survival and 0.790 for predicting favorable outcome using EEG features. Adding clinical features did not improve the overall performance of the classifier (for survival: AUC = 0.806, p = 0.926; for favorable outcome: AUC = 0.777, p = 0.844). Survival could be predicted in all etiology groups: the AUC was 0.958 for patients with HIE, 0.955 for patients with TBI and other neurosurgical diagnoses, 0.697 for patients with metabolic, inflammatory or infectious causes for consciousness impairment and 0.695 for patients with stroke. Training the classifier separately on subgroups of patients with a given etiology (and thus using less training data) leads to poorer classification performance. CONCLUSIONS While prognostication was best for patients with HIE and TBI, our study demonstrates that similar EEG criteria can be used in patients with various causes of consciousness impairment, and that the size of the training set is more important than homogeneity of ACI etiology.
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15
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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: 0.6] [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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