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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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Lang M, Kenda M, Scheel M, Martola J, Wheeler M, Owen S, Johnsson M, Annborn M, Dankiewicz J, Deye N, Düring J, Friberg H, Halliday T, Jakobsen JC, Lascarrou JB, Levin H, Lilja G, Lybeck A, McGuigan P, Rylander C, Sem V, Thomas M, Ullén S, Undén J, Wise MP, Cronberg T, Wassélius J, Nielsen N, Leithner C, Moseby-Knappe M. Standardised and automated assessment of head computed tomography reliably predicts poor functional outcome after cardiac arrest: a prospective multicentre study. Intensive Care Med 2024; 50:1096-1107. [PMID: 38900283 PMCID: PMC11245448 DOI: 10.1007/s00134-024-07497-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024]
Abstract
PURPOSE Application of standardised and automated assessments of head computed tomography (CT) for neuroprognostication after out-of-hospital cardiac arrest. METHODS Prospective, international, multicentre, observational study within the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Routine CTs from adult unconscious patients obtained > 48 h ≤ 7 days post-arrest were assessed qualitatively and quantitatively by seven international raters blinded to clinical information using a pre-published protocol. Grey-white-matter ratio (GWR) was calculated from four (GWR-4) and eight (GWR-8) regions of interest manually placed at the basal ganglia level. Additionally, GWR was obtained using an automated atlas-based approach. Prognostic accuracies for prediction of poor functional outcome (modified Rankin Scale 4-6) for the qualitative assessment and for the pre-defined GWR cutoff < 1.10 were calculated. RESULTS 140 unconscious patients were included; median age was 68 years (interquartile range [IQR] 59-76), 76% were male, and 75% had poor outcome. Standardised qualitative assessment and all GWR models predicted poor outcome with 100% specificity (95% confidence interval [CI] 90-100). Sensitivity in median was 37% for the standardised qualitative assessment, 39% for GWR-8, 30% for GWR-4 and 41% for automated GWR. GWR-8 was superior to GWR-4 regarding prognostic accuracies, intra- and interrater agreement. Overall prognostic accuracy for automated GWR (area under the curve [AUC] 0.84, 95% CI 0.77-0.91) did not significantly differ from manually obtained GWR. CONCLUSION Standardised qualitative and quantitative assessments of CT are reliable and feasible methods to predict poor functional outcome after cardiac arrest. Automated GWR has the potential to make CT quantification for neuroprognostication accessible to all centres treating cardiac arrest patients.
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Affiliation(s)
- Margareta Lang
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Radiology, Helsingborg Hospital, Helsingborg, Sweden
| | - Martin Kenda
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Junior Digital Clinician Scientist Program, Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität and Humboldt-Universität Zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Michael Scheel
- Department of Neuroradiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Juha Martola
- HUS Medical Imaging Center, Radiology, University of Helsinki, Helsinki University Hospital, Helsinki, Finland
| | - Matthew Wheeler
- University Hospital of Wales, Cardiff and Vale University Health Board, Cardiff, Wales, UK
| | - Stephanie Owen
- University Hospital of Wales, Cardiff and Vale University Health Board, Cardiff, Wales, UK
| | - Mikael Johnsson
- Department of Radiology, Helsingborg Hospital, Helsingborg, Sweden
| | - Martin Annborn
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Anaesthesia and Intensive Care, Helsingborg Hospital, Helsingborg, Sweden
| | - Josef Dankiewicz
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Cardiology, Skåne University Hospital, Lund, Sweden
| | - Nicolas Deye
- Department of Medical and Toxicological Intensive Care Unit, Inserm UMR-S 942, Assistance Publique des Hopitaux de Paris, Lariboisière University Hospital, Paris, France
| | - Joachim Düring
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Anaesthesia and Intensive Care, Skåne University Hospital, Malmö, Sweden
| | - Hans Friberg
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Anaesthesia and Intensive Care, Skåne University Hospital, Malmö, Sweden
| | - Thomas Halliday
- Department of Operation and Intensive Care, Linköping University Hospital, Linköping, Sweden
| | - Janus Christian Jakobsen
- Department of Regional Health Research, The Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
- Copenhagen Trial Unit, Centre for Clinical Intervention Research, Capital Region of Denmark, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Jean-Baptiste Lascarrou
- Medecine Intensive Reanimation, Movement-Interactions-Performance,, Nantes Université, CHU Nantes, MIP, UR 4334, 44000, Nantes, France
| | - Helena Levin
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Research and Education, Skåne University Hospital, Lund, Sweden
| | - Gisela Lilja
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Anna Lybeck
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Anaesthesia and Intensive Care, Skåne University Hospital, Lund, Sweden
| | - Peter McGuigan
- Regional Intensive Care Unit, Royal Victoria Hospital, Belfast, UK
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, UK
| | - Christian Rylander
- Anaesthesia and Intensive Care, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Victoria Sem
- Department of Anaesthesia and Intensive Care, Central Hospital of Karlstad, Karlstad, Sweden
| | - Matthew Thomas
- Intensive Care Unit, University Hospitals Bristol and Weston, Bristol, UK
| | - Susann Ullén
- Clinical Studies Sweden‑Forum South, Skåne University Hospital, Lund, Sweden
| | - Johan Undén
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Operation and Intensive Care, Hallands Hospital Halmstad, Halmstad, Sweden
| | - Matt P Wise
- Adult Critical Care, University Hospital of Wales, Cardiff, UK
| | - Tobias Cronberg
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Johan Wassélius
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Niklas Nielsen
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Anaesthesia and Intensive Care, Helsingborg Hospital, Helsingborg, Sweden
| | - Christoph Leithner
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität and Humboldt-Universität Zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Marion Moseby-Knappe
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
- Department of Neurology, Skåne University Hospital, Lund, Sweden.
- Department of Rehabilitation, Skåne University Hospital, 22185, Lund, Sweden.
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Jung YH, Lee HY, Lee BK, Choi BK, Kim TH, Kim JW, Kim HC, Kim HJ, Jeung KW. Feasibility of Magnetic Resonance-Based Conductivity Imaging as a Tool to Estimate the Severity of Hypoxic-Ischemic Brain Injury in the First Hours After Cardiac Arrest. Neurocrit Care 2024; 40:538-550. [PMID: 37353670 DOI: 10.1007/s12028-023-01776-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/05/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND Early identification of the severity of hypoxic-ischemic brain injury (HIBI) after cardiac arrest can be used to help plan appropriate subsequent therapy. We evaluated whether conductivity of cerebral tissue measured using magnetic resonance-based conductivity imaging (MRCI), which provides contrast derived from the concentration and mobility of ions within the imaged tissue, can reflect the severity of HIBI in the early hours after cardiac arrest. METHODS Fourteen minipigs were resuscitated after 5 min or 12 min of untreated cardiac arrest. MRCI was performed at baseline and at 1 h and 3.5 h after return of spontaneous circulation (ROSC). RESULTS In both groups, the conductivity of cerebral tissue significantly increased at 1 h after ROSC compared with that at baseline (P = 0.031 and 0.016 in the 5-min and 12-min groups, respectively). The increase was greater in the 12-min group, resulting in significantly higher conductivity values in the 12-min group (P = 0.030). At 3.5 h after ROSC, the conductivity of cerebral tissue in the 12-min group remained increased (P = 0.022), whereas that in the 5-min group returned to its baseline level. CONCLUSIONS The conductivity of cerebral tissue was increased in the first hours after ROSC, and the increase was more prominent and lasted longer in the 12-min group than in the 5-min group. Our findings suggest the promising potential of MRCI as a tool to estimate the severity of HIBI in the early hours after cardiac arrest.
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Affiliation(s)
- Yong Hun Jung
- Department of Emergency Medicine, Chonnam National University Hospital, 42 Jebong-ro, Donggu, Gwangju, 61469, Republic of Korea
- Department of Emergency Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Hyoung Youn Lee
- Trauma Center, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Byung Kook Lee
- Department of Emergency Medicine, Chonnam National University Hospital, 42 Jebong-ro, Donggu, Gwangju, 61469, Republic of Korea
- Department of Emergency Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Bup Kyung Choi
- Medical Science Research Institute, Kyung Hee University Hospital, Seoul, Republic of Korea
| | - Tae-Hoon Kim
- Medical Convergence Research Center, Wonkwang University, Iksan, Republic of Korea
| | - Jin Woong Kim
- Department of Radiology, Chosun University Hospital, Gwangju, Republic of Korea
| | - Hyun Chul Kim
- Department of Radiology, Chosun University Hospital, Gwangju, Republic of Korea
| | - Hyung Joong Kim
- Medical Science Research Institute, Kyung Hee University Hospital, Seoul, Republic of Korea
| | - Kyung Woon Jeung
- Department of Emergency Medicine, Chonnam National University Hospital, 42 Jebong-ro, Donggu, Gwangju, 61469, Republic of Korea.
- Department of Emergency Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea.
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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 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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Nikolovski SS, Lazic AD, Fiser ZZ, Obradovic IA, Tijanic JZ, Raffay V. Recovery and Survival of Patients After Out-of-Hospital Cardiac Arrest: A Literature Review Showcasing the Big Picture of Intensive Care Unit-Related Factors. Cureus 2024; 16:e54827. [PMID: 38529434 PMCID: PMC10962929 DOI: 10.7759/cureus.54827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2024] [Indexed: 03/27/2024] Open
Abstract
As an important public health issue, out-of-hospital cardiac arrest (OHCA) requires several stages of high quality medical care, both on-field and after hospital admission. Post-cardiac arrest shock can lead to severe neurological injury, resulting in poor recovery outcome and increased risk of death. These characteristics make this condition one of the most important issues to deal with in post-OHCA patients hospitalized in intensive care units (ICUs). Also, the majority of initial post-resuscitation survivors have underlying coronary diseases making revascularization procedure another crucial step in early management of these patients. Besides keeping myocardial blood flow at a satisfactory level, other tissues must not be neglected as well, and maintaining mean arterial pressure within optimal range is also preferable. All these procedures can be simplified to a certain level along with using targeted temperature management methods in order to decrease metabolic demands in ICU-hospitalized post-OHCA patients. Additionally, withdrawal of life-sustaining therapy as a controversial ethical topic is under constant re-evaluation due to its possible influence on overall mortality rates in patients initially surviving OHCA. Focusing on all of these important points in process of managing ICU patients is an imperative towards better survival and complete recovery rates.
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Affiliation(s)
- Srdjan S Nikolovski
- Pathology and Laboratory Medicine, Cardiovascular Research Institute, Loyola University Chicago Health Science Campus, Maywood, USA
- Emergency Medicine, Serbian Resuscitation Council, Novi Sad, SRB
| | - Aleksandra D Lazic
- Emergency Center, Clinical Center of Vojvodina, Novi Sad, SRB
- Emergency Medicine, Serbian Resuscitation Council, Novi Sad, SRB
| | - Zoran Z Fiser
- Emergency Medicine, Department of Emergency Medicine, Novi Sad, SRB
| | - Ivana A Obradovic
- Anesthesiology, Resuscitation, and Intensive Care, Sveti Vračevi Hospital, Bijeljina, BIH
| | - Jelena Z Tijanic
- Emergency Medicine, Municipal Institute of Emergency Medicine, Kragujevac, SRB
| | - Violetta Raffay
- School of Medicine, European University Cyprus, Nicosia, CYP
- Emergency Medicine, Serbian Resuscitation Council, Novi Sad, SRB
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6
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Kim J, Lee JH. Quantitative cistern effacement and reduced gray to white matter ratio for prognostication in early brain computed tomography of patients with cardiac arrest. Heliyon 2024; 10:e23741. [PMID: 38187337 PMCID: PMC10767505 DOI: 10.1016/j.heliyon.2023.e23741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 10/15/2023] [Accepted: 12/12/2023] [Indexed: 01/09/2024] Open
Abstract
Background The impact of cerebral edema on brain cells and ventricles in cardiac arrest patients can manifest as effacement of cortical sulci, diminished ventricle size, altered gray matter to white matter ratio (GWR), and increased optic nerve sheath diameter (ONSD) in brain CT scans. However, a complete investigation of GWR in whole lobes, quantitative cistern size, and comprehensive comparison of various brain CT parameters has not been conducted. This study aimed to comprehensively compare various early brain CT parameters along with conventional significant variables in relation to poor neurological outcome and diffuse cortical necrosis. Methods This retrospective study included 86 adult patients with cardiac arrest who underwent brain CT/MRI. GWRs, the distance of the posterior ambient cistern, and ONSD in early brain CT and regions of interest (ROIs) in brain MRI were measured and analyzed along with clinical characteristics. Results ROIs in the putamen and parietal white matter showed significant differences (p = 0.05, p = 0.022, respectively). The distance of the posterior ambient cistern and the GWR of the putamen and parietal white matter were newly developed predictors that were not used previously and demonstrated a significant correlation with the presence of diffuse cortical necrosis (OR 0.4, p = 0.006, AUC 0.637; OR 0.478, p = 0.02, AUC 0.603, respectively) or poor neurological outcomes (AUC 0.637, AUC 0.603, respectively), but were not more significant than pupil reflex (OR 0.06, p < 0.001). ONSD was not significantly associated with the outcomes. Conclusions Quantitative cistern effacement and reduced GWR of the putamen and parietal white matter in early brain CT measurements of cardiac arrest patients were promising predictors in early brain CT for prognostication, but compared with clinical characteristics, the clinical significance of the CT predictors was not considerable. The relationship and clinical significance between the parameters in early brain CT and the outcomes might have to be separately considered.
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Affiliation(s)
- Jinsung Kim
- Department of Emergency Medicine, Dong-A University College of Medicine, Busan, South Korea
| | - Jae Hoon Lee
- Department of Emergency Medicine, Dong-A University College of Medicine, Busan, South Korea
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7
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Robba C, Zanier ER, Lopez Soto C, Park S, Sonneville R, Helbolk R, Sarwal A, Newcombe VFJ, van der Jagt M, Gunst J, Gauss T, Figueiredo S, Duranteau J, Skrifvars MB, Iaquaniello C, Muehlschlegel S, Metaxa V, Sandroni C, Citerio G, Meyfroidt G. Mastering the brain in critical conditions: an update. Intensive Care Med Exp 2024; 12:1. [PMID: 38182945 PMCID: PMC10770006 DOI: 10.1186/s40635-023-00587-3] [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: 11/28/2023] [Accepted: 12/18/2023] [Indexed: 01/07/2024] Open
Abstract
Acute brain injuries, such as traumatic brain injury and ischemic and hemorragic stroke, are a leading cause of death and disability worldwide. While characterized by clearly distict primary events-vascular damage in strokes and biomechanical damage in traumatic brain injuries-they share common secondary injury mechanisms influencing long-term outcomes. Growing evidence suggests that a more personalized approach to optimize energy substrate delivery to the injured brain and prognosticate towards families could be beneficial. In this context, continuous invasive and/or non-invasive neuromonitoring, together with clinical evaluation and neuroimaging to support strategies that optimize cerebral blood flow and metabolic delivery, as well as approaches to neuroprognostication are gaining interest. Recently, the European Society of Intensive Care Medicine organized a 2-day course focused on a practical case-based clinical approach of acute brain-injured patients in different scenarios and on future perspectives to advance the management of this population. The aim of this manuscript is to update clinicians dealing with acute brain injured patients in the intensive care unit, describing current knowledge and clinical practice based on the insights presented during this course.
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Affiliation(s)
- Chiara Robba
- Anesthesia and Intensive Care, San Martino Policlinico Hospital, IRCCS for Oncology and Neurosciences, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics, University of Genoa, Genoa, Italy
| | - Elisa R Zanier
- Department of Acute Brain and Cardiovascular Injury, Mario Negri Institute for Pharmacological Research IRCCS, Milan, Italy.
| | - Carmen Lopez Soto
- Department of Critical Care, King's College Hospital NHS Foundation Trust, London, SE5 9RS, UK
| | - Soojin Park
- Departments of Neurology and Biomedical Informatics, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Romain Sonneville
- Department of Intensive Care Medicine, Hôpital Bichat-Claude Bernard, Université Paris Cité, INSERM UMR 1137, IAME, APHP.Nord, Paris, France
| | - Raimund Helbolk
- Neurological Intensive Care Unit, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
- Department of Neurology, Johannes Kepler University, Linz, Austria
- Clinical Research Institute Neuroscience, Johannes Kepler University, Linz, Austria
| | - Aarti Sarwal
- Wake Forest Baptist Health Center, Winston-Salem, NC, USA
| | | | - Mathieu van der Jagt
- Department of Intensive Care Adults, Erasmus MC-University Medical Centre, Room Ne-415, PO BOX 2040, 3000 CA, Rotterdam, The Netherlands
| | - Jan Gunst
- Department of Intensive Care Medicine, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Intensive Care Medicine, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Tobias Gauss
- Department of Anaesthesia and Intensive Care, Centre Hospitalier Universitaire Grenoble, Universitaire Grenoble Alpes, Grenoble, France
- INSERM U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Samy Figueiredo
- Department of Anaesthesiology and Critical Care Medicine, Bicêtre Hospital, Université Paris-Saclay, Assistance Publique des Hôpitaux de Paris, Équipe DYNAMIC, Inserm UMR 999, Le Kremlin-Bicêtre, France
| | - Jacques Duranteau
- Department of Anaesthesiology and Critical Care Medicine, Bicêtre Hospital, Université Paris-Saclay, Assistance Publique des Hôpitaux de Paris, Équipe DYNAMIC, Inserm UMR 999, Le Kremlin-Bicêtre, France
| | - Markus B Skrifvars
- Department of Emergency Care and Services, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Carolina Iaquaniello
- Neuroanesthesia and Intensive Care, Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Susanne Muehlschlegel
- Division of Neurosciences Critical Care, Departments of Neurology and Anesthesiology/Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Victoria Metaxa
- Department of Critical Care, King's College Hospital NHS Foundation Trust, London, SE5 9RS, UK
| | - Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy
- Institute of Anaesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giuseppe Citerio
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Geert Meyfroidt
- Department of Intensive Care Medicine, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Intensive Care Medicine, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
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8
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Sohn G, Kim SE. Measurement of thalamus and cortical damages in hypoxic ischemic encephalopathy. IBRO Neurosci Rep 2023; 15:179-185. [PMID: 37731916 PMCID: PMC10507579 DOI: 10.1016/j.ibneur.2023.09.002] [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: 03/12/2023] [Revised: 09/04/2023] [Accepted: 09/04/2023] [Indexed: 09/22/2023] Open
Abstract
Background The thalamic gray-white matter ratios (GWRs) on CT and quantitative suppression ratios (SRs) of background activities on EEG may reflect damages in the thalamus and cerebral hemispheres in patients with hypoxic-ischemic encephalopathy (HIE). Methods The inclusion criteria were (1) cardiac arrest patients over the age of 20 years from March 2010 to March 2020, and (2) patients who had both EEG and brain CT within 7 days after cardiac arrest. The thalamic GWRs were semi-quantitatively measured by using the region of interest (ROI). SRs of background were analyzed with the installed software (Persyst® v13) in EEG machine. Results 175 patients were included among 686 patients with HIE and the thalamic GWRs of 168 patients were successfully measured. 155 patients (89 %) showed poor outcomes. The poor outcome group revealed not only higher SRs, but also lower thalamic GWRs. The thalamic GWRs showed a negative correlation to the SRs (ρ (rho) = -0.36, p < 0.0001 for right side, ρ (rho) = -0.31, p < 0.0001 for left side). The good outcome group showed neither beyond the cut-off values of thalamic GWRs nor SRs [40 % (59/148) VS 0 % (0/20) in right side, p = 0.0005 %, and 28 % (42/148) VS 0 % (0/20) in left side, p = 0.0061]. Conclusion The thalamic GWRs and SRs may reflect the damage in the thalamus and cerebral hemispheres in patients with HIE. Insults in the thalamocortical circuit (TCC) or the thalamus might be responsible for the poor outcome.
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Affiliation(s)
| | - Sung Eun Kim
- Correspondence to: Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan 48108, Republic of Korea.
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Kimberly WT, Sorby-Adams AJ, Webb AG, Wu EX, Beekman R, Bowry R, Schiff SJ, de Havenon A, Shen FX, Sze G, Schaefer P, Iglesias JE, Rosen MS, Sheth KN. Brain imaging with portable low-field MRI. NATURE REVIEWS BIOENGINEERING 2023; 1:617-630. [PMID: 37705717 PMCID: PMC10497072 DOI: 10.1038/s44222-023-00086-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/06/2023] [Indexed: 09/15/2023]
Abstract
The advent of portable, low-field MRI (LF-MRI) heralds new opportunities in neuroimaging. Low power requirements and transportability have enabled scanning outside the controlled environment of a conventional MRI suite, enhancing access to neuroimaging for indications that are not well suited to existing technologies. Maximizing the information extracted from the reduced signal-to-noise ratio of LF-MRI is crucial to developing clinically useful diagnostic images. Progress in electromagnetic noise cancellation and machine learning reconstruction algorithms from sparse k-space data as well as new approaches to image enhancement have now enabled these advancements. Coupling technological innovation with bedside imaging creates new prospects in visualizing the healthy brain and detecting acute and chronic pathological changes. Ongoing development of hardware, improvements in pulse sequences and image reconstruction, and validation of clinical utility will continue to accelerate this field. As further innovation occurs, portable LF-MRI will facilitate the democratization of MRI and create new applications not previously feasible with conventional systems.
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Affiliation(s)
- W Taylor Kimberly
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Annabel J Sorby-Adams
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew G Webb
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Rachel Beekman
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale New Haven Hospital and Yale School of Medicine, Yale Center for Brain & Mind Health, New Haven, CT, USA
| | - Ritvij Bowry
- Departments of Neurosurgery and Neurology, McGovern Medical School, University of Texas Health Neurosciences, Houston, TX, USA
| | - Steven J Schiff
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Adam de Havenon
- Division of Vascular Neurology, Department of Neurology, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT, USA
| | - Francis X Shen
- Harvard Medical School Center for Bioethics, Harvard law School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Gordon Sze
- Department of Radiology, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT, USA
| | - Pamela Schaefer
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Centre for Medical Image Computing, University College London, London, UK
- Computer Science and AI Laboratory, Massachusetts Institute of Technology, Boston, MA, USA
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kevin N Sheth
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale New Haven Hospital and Yale School of Medicine, Yale Center for Brain & Mind Health, New Haven, CT, USA
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10
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Wilcox J, Redwood S, Patterson T. Cardiac arrest centres: what do they add? Resuscitation 2023:109865. [PMID: 37315916 DOI: 10.1016/j.resuscitation.2023.109865] [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: 05/09/2023] [Revised: 05/28/2023] [Accepted: 05/30/2023] [Indexed: 06/16/2023]
Abstract
There are wide regional variations in outcome following resuscitated out of hospital cardiac arrest. These geographical differences appear to be due to hospital infrastructure and provider experience rather than baseline characteristics. It is proposed that post-arrest care be delivered in a systematic fashion by concentrating services in Cardiac Arrest Centres, with greater provider experience, 24-hour access to diagnostics, and specialist treatment to minimise the impact of ischaemia-reperfusion injury and treat the causative pathology. These cardiac arrest centres would provide access to targeted critical care, acute cardiac care, radiology services and appropriate neuro-prognostication. However implementation of cardiac arrest networks with specialist receiving hospitals is complex and requires alignment of pre-hospital care services with those delivered in hospital. Furthermore there are no randomised trial data currently supporting pre-hospital delivery to a Cardiac Arrest Centre and definitions are heterogeneous. In this review article, we propose a universal definition of a Cardiac Arrest Centre and review the current observational data evidence and the potential impact of the ARREST trial.
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Affiliation(s)
- Joshua Wilcox
- Cardiovascular Department, Guy's and St. Thomas' NHS Foundation Trust.
| | - Simon Redwood
- Cardiovascular Department, Guy's and St. Thomas' NHS Foundation Trust; Cardiovascular, FOLSM, King's College London
| | - Tiffany Patterson
- Cardiovascular Department, Guy's and St. Thomas' NHS Foundation Trust
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11
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Rajajee V, Muehlschlegel S, Wartenberg KE, Alexander SA, Busl KM, Chou SHY, Creutzfeldt CJ, Fontaine GV, Fried H, Hocker SE, Hwang DY, Kim KS, Madzar D, Mahanes D, Mainali S, Meixensberger J, Montellano F, Sakowitz OW, Weimar C, Westermaier T, Varelas PN. Guidelines for Neuroprognostication in Comatose Adult Survivors of Cardiac Arrest. Neurocrit Care 2023; 38:533-563. [PMID: 36949360 PMCID: PMC10241762 DOI: 10.1007/s12028-023-01688-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 01/30/2023] [Indexed: 03/24/2023]
Abstract
BACKGROUND Among cardiac arrest survivors, about half remain comatose 72 h following return of spontaneous circulation (ROSC). Prognostication of poor neurological outcome in this population may result in withdrawal of life-sustaining therapy and death. The objective of this article is to provide recommendations on the reliability of select clinical predictors that serve as the basis of neuroprognostication and provide guidance to clinicians counseling surrogates of comatose cardiac arrest survivors. METHODS A narrative systematic review was completed using Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. Candidate predictors, which included clinical variables and prediction models, were selected based on clinical relevance and the presence of an appropriate body of evidence. The Population, Intervention, Comparator, Outcome, Timing, Setting (PICOTS) question was framed as follows: "When counseling surrogates of comatose adult survivors of cardiac arrest, should [predictor, with time of assessment if appropriate] be considered a reliable predictor of poor functional outcome assessed at 3 months or later?" Additional full-text screening criteria were used to exclude small and lower-quality studies. Following construction of the evidence profile and summary of findings, recommendations were based on four GRADE criteria: quality of evidence, balance of desirable and undesirable consequences, values and preferences, and resource use. In addition, good practice recommendations addressed essential principles of neuroprognostication that could not be framed in PICOTS format. RESULTS Eleven candidate clinical variables and three prediction models were selected based on clinical relevance and the presence of an appropriate body of literature. A total of 72 articles met our eligibility criteria to guide recommendations. Good practice recommendations include waiting 72 h following ROSC/rewarming prior to neuroprognostication, avoiding sedation or other confounders, the use of multimodal assessment, and an extended period of observation for awakening in patients with an indeterminate prognosis, if consistent with goals of care. The bilateral absence of pupillary light response > 72 h from ROSC and the bilateral absence of N20 response on somatosensory evoked potential testing were identified as reliable predictors. Computed tomography or magnetic resonance imaging of the brain > 48 h from ROSC and electroencephalography > 72 h from ROSC were identified as moderately reliable predictors. CONCLUSIONS These guidelines provide recommendations on the reliability of predictors of poor outcome in the context of counseling surrogates of comatose survivors of cardiac arrest and suggest broad principles of neuroprognostication. Few predictors were considered reliable or moderately reliable based on the available body of evidence.
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Affiliation(s)
- Venkatakrishna Rajajee
- Departments of Neurology and Neurosurgery, 3552 Taubman Health Care Center, SPC 5338, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5338, USA.
| | - Susanne Muehlschlegel
- Departments of Neurology, Anesthesiology, and Surgery, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | | | - Katharina M Busl
- Departments of Neurology and Neurosurgery, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Sherry H Y Chou
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Gabriel V Fontaine
- Departments of Pharmacy and Neurosciences, Intermountain Healthcare, Salt Lake City, UT, USA
| | - Herbert Fried
- Department of Neurosurgery, Denver Health Medical Center, Denver, CO, USA
| | - Sara E Hocker
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - David Y Hwang
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Keri S Kim
- Pharmacy Practice, University of Illinois, Chicago, IL, USA
| | - Dominik Madzar
- Department of Neurology, University of Erlangen, Erlangen, Germany
| | - Dea Mahanes
- Departments of Neurology and Neurosurgery, University of Virginia Health, Charlottesville, VA, USA
| | - Shraddha Mainali
- Department of Neurology, Virginia Commonwealth University, Richmond, VA, USA
| | | | | | - Oliver W Sakowitz
- Department of Neurosurgery, Neurosurgery Center Ludwigsburg-Heilbronn, Ludwigsburg, Germany
| | - Christian Weimar
- Institute of Medical Informatics, Biometry, and Epidemiology, University Hospital Essen, Essen, Germany
- BDH-Clinic Elzach, Elzach, Germany
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12
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Fordyce CB, Kramer AH, Ainsworth C, Christenson J, Hunter G, Kromm J, Lopez Soto C, Scales DC, Sekhon M, van Diepen S, Dragoi L, Josephson C, Kutsogiannis J, Le May MR, Overgaard CB, Savard M, Schnell G, Wong GC, Belley-Côté E, Fantaneanu TA, Granger CB, Luk A, Mathew R, McCredie V, Murphy L, Teitelbaum J. Neuroprognostication in the Post Cardiac Arrest Patient: A Canadian Cardiovascular Society Position Statement. Can J Cardiol 2023; 39:366-380. [PMID: 37028905 DOI: 10.1016/j.cjca.2022.12.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 04/08/2023] Open
Abstract
Cardiac arrest (CA) is associated with a low rate of survival with favourable neurologic recovery. The most common mechanism of death after successful resuscitation from CA is withdrawal of life-sustaining measures on the basis of perceived poor neurologic prognosis due to underlying hypoxic-ischemic brain injury. Neuroprognostication is an important component of the care pathway for CA patients admitted to hospital but is complex, challenging, and often guided by limited evidence. Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to evaluate the evidence underlying factors or diagnostic modalities available to determine prognosis, recommendations were generated in the following domains: (1) circumstances immediately after CA; (2) focused neurologic exam; (3) myoclonus and seizures; (4) serum biomarkers; (5) neuroimaging; (6) neurophysiologic testing; and (7) multimodal neuroprognostication. This position statement aims to serve as a practical guide to enhance in-hospital care of CA patients and emphasizes the adoption of a systematic, multimodal approach to neuroprognostication. It also highlights evidence gaps.
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Affiliation(s)
- Christopher B Fordyce
- Division of Cardiology, Department of Medicine, Vancouver General Hospital, and the Centre for Cardiovascular Innovation, University of British Columbia, Vancouver, British Columbia.
| | - Andreas H Kramer
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta; Department of Critical Care, University of Calgary, Alberta
| | - Craig Ainsworth
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Jim Christenson
- Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia
| | - Gary Hunter
- Division of Neurology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Julie Kromm
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta; Department of Critical Care, University of Calgary, Alberta
| | - Carmen Lopez Soto
- Department of Critical Care, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Damon C Scales
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Mypinder Sekhon
- Division of Critical Care, Department of Medicine, Vancouver General Hospital, Djavad Mowafaghian Centre for Brain Health, International Centre for Repair Discoveries, University of British Columbia, Vancouver, British Columbia
| | - Sean van Diepen
- Department of Critical Care Medicine, University of Alberta, Edmonton, Alberta; Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, Alberta
| | - Laura Dragoi
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Colin Josephson
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta; Department of Critical Care, University of Calgary, Alberta
| | - Jim Kutsogiannis
- Department of Critical Care Medicine, University of Alberta, Edmonton, Alberta
| | - Michel R Le May
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Christopher B Overgaard
- Division of Cardiology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Martin Savard
- Department of Neurological Sciences CHU de Québec - Hôpital de l'Enfant-Jésus Quebec City, Quebec, Canada
| | - Gregory Schnell
- Division of Cardiology, Department of Medicine, University of Calgary, Calgary, Alberta
| | - Graham C Wong
- Division of Cardiology, Department of Medicine, Vancouver General Hospital, and the Centre for Cardiovascular Innovation, University of British Columbia, Vancouver, British Columbia
| | - Emilie Belley-Côté
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Tadeu A Fantaneanu
- Division of Neurology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | | | - Adriana Luk
- Division of Cardiology, Department of Medicine, University of Toronto and the Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Rebecca Mathew
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, and the Faculty of Medicine, Division of Critical Care, University of Ottawa, Ottawa, Ontario, Canada
| | - Victoria McCredie
- Interdepartmental Division of Critical Care Medicine, University of Toronto, the Krembil Research Institute, Toronto Western Hospital, University Health Network, and Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Laurel Murphy
- Departments of Emergency Medicine and Critical Care, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jeanne Teitelbaum
- Neurological Intensive Care Unit, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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13
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Park JY, Kim YH, Ahn SJ, Lee JH, Lee DW, Hwang SY, Song YG. Association between the extent of diffusion restriction on brain diffusion-weighted imaging and neurological outcomes after an out-of-hospital cardiac arrest. Resuscitation 2023; 187:109761. [PMID: 36898602 DOI: 10.1016/j.resuscitation.2023.109761] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 02/16/2023] [Accepted: 03/01/2023] [Indexed: 03/11/2023]
Abstract
BACKGROUND This study evaluated the association between the extent of diffusion restriction on brain diffusion-weighted imaging (DWI) and neurological outcomes in patients who underwent targeted temperature management (TTM) after an out-of-hospital cardiac arrest (OHCA). METHODS Patients who underwent brain magnetic resonance imaging within 10 days of OHCA between 2012 and 2021 were analysed. The extent of diffusion restriction was described according to the modified DWI Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS). The 35 predefined brain regions were assigned a score if diffuse signal changes were concordantly present in DWI scans and apparent diffusion coefficient maps. The primary outcome was an unfavourable neurological outcome at 6 months. The sensitivity, specificity, and receiver operating characteristic (ROC) curves for the measured parameters were analysed. Cut-off values were determined to predict the primary outcome. The predictive cut-off DWI-ASPECTS was internally validated using five-fold cross-validation. RESULTS Of the 301 patients, 108 (35.9%) had 6-month favourable neurological outcomes. Patients with unfavourable outcomes had higher whole-brain DWI-ASPECTS (median, 31 [26-33] vs. 0 [0-1], P < 0.001) than those with favourable outcomes. The area under the ROC curve (AUROC) of whole-brain DWI-ASPECTS was 0.957 (95% confidence interval [CI] 0.928-0.977). A cut-off value of ≥8 for unfavourable neurological outcomes had specificity and sensitivity of 100% (95% CI 96.6-100) and 89.6% (95% CI 84.4-93.6), respectively. The mean AUROC was 0.956. CONCLUSION More extensive diffusion restriction on DWI-ASPECTS in patients with OHCA who underwent TTM was associated with 6-month unfavourable neurological outcomes. Running title: Diffusion restriction and neurological outcomes after cardiac arrest.
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Affiliation(s)
- Jong Yoon Park
- Department of Emergency Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, South Korea
| | - Yong Hwan Kim
- Department of Emergency Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, South Korea.
| | - Seong Jun Ahn
- Department of Emergency Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, South Korea
| | - Jun Ho Lee
- Department of Emergency Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, South Korea
| | - Dong Woo Lee
- Department of Emergency Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, South Korea
| | - Seong Youn Hwang
- Department of Emergency Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, South Korea
| | - Yun Gyu Song
- Department of Radiology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, South Korea
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14
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Kirschen MP, Berman JI, Liu H, Ouyang M, Mondal A, Griffis H, Levow C, Winters M, Lang SS, Huh J, Huang H, Berg RA, Vossough A, Topjian A. Association Between Quantitative Diffusion-Weighted Magnetic Resonance Neuroimaging and Outcome After Pediatric Cardiac Arrest. Neurology 2022; 99:e2615-e2626. [PMID: 36028319 PMCID: PMC9754647 DOI: 10.1212/wnl.0000000000201189] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/15/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Diffusion MRI can quantify the extent of hypoxic-ischemic brain injury after cardiac arrest. Our objective was to determine the association between the adult-derived threshold of apparent diffusion coefficient (ADC) <650 × 10-6 mm2/s in >10% of brain tissue and an unfavorable outcome after pediatric cardiac arrest. Since ADC decreases exponentially as a function of increasing age, we determined the association between (1) having >10% of brain tissue below a novel age-dependent ADC threshold, and (2) age-normalized whole-brain mean ADC and unfavorable outcome. METHODS This was a retrospective study of patients aged ≤18 years who had cardiac arrest and a clinically obtained brain MRI within 7 days. The primary outcome was unfavorable neurologic status at hospital discharge based on the Pediatric Cerebral Performance Category score. ADC images were extracted from 3-direction diffusion imaging. We determined whether each patient had >10% of voxels with an ADC below prespecified thresholds. We computed the whole-brain mean ADC for each patient. RESULTS One hundred thirty-four patients were analyzed. Patients with ADC <650 × 10-6 mm2/s in >10% of voxels had 15 times higher odds (95% CI 5-65) of an unfavorable outcome compared with patients with ADC <650 × 10-6 mm2/s (area under the receiver operating characteristic curve [AUROC] 0.72 [95% CI 0.63-0.80]). These ADC criteria had a sensitivity and specificity of 0.49 and 0.94, respectively, and positive and negative predictive values of 0.93 and 0.52, respectively, for an unfavorable outcome. The age-dependent ADC threshold that yielded optimal sensitivity and specificity for unfavorable outcomes was <300 × 10-6 mm2/s below each patient's predicted whole-brain mean ADC. The sensitivity, specificity, and positive and negative predictive values for this ADC threshold were 0.53, 0.96, 0.96, and 0.54, respectively (odds ratio [OR] 26.4 [95% CI 7.5-168.3]; AUROC 0.74 [95% CI 0.66-0.83]). Lower age-normalized whole-brain mean ADC was also associated with an unfavorable outcome (OR 0.42 [0.24-0.64], AUROC 0.76 [95% CI 0.66-0.82]). DISCUSSION Quantitative diffusion thresholds on MRI within 7 days after cardiac arrest were associated with an unfavorable outcome in children. The age-independent ADC threshold was highly specific for predicting an unfavorable outcome. However, the specificity and sensitivity increased when using age-dependent ADC thresholds. Age-dependent ADC thresholds may improve prognostic accuracy and require further investigation in larger cohorts. CLASSIFICATION OF EVIDENCE This study provides Class III evidence that quantitative diffusion-weighted imaging within 7 days postarrest can predict an unfavorable clinical outcome in children.
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Affiliation(s)
- Matthew P Kirschen
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia.
| | - Jeffrey I Berman
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Hongyan Liu
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Minhui Ouyang
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Antara Mondal
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Heather Griffis
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Cindee Levow
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Madeline Winters
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Shih-Shan Lang
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Jimmy Huh
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Hao Huang
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Robert A Berg
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Arastoo Vossough
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Alexis Topjian
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
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15
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Kenda M, Cheng Z, Guettler C, Storm C, Ploner CJ, Leithner C, Scheel M. Inter-rater agreement between humans and computer in quantitative assessment of computed tomography after cardiac arrest. Front Neurol 2022; 13:990208. [PMID: 36313501 PMCID: PMC9606648 DOI: 10.3389/fneur.2022.990208] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 08/12/2022] [Indexed: 11/16/2022] Open
Abstract
Background Head computed tomography (CT) is used to predict neurological outcome after cardiac arrest (CA). The current reference standard includes quantitative image analysis by a neuroradiologist to determine the Gray-White-Matter Ratio (GWR) which is calculated via the manual measurement of radiodensity in different brain regions. Recently, automated analysis methods have been introduced. There is limited data on the Inter-rater agreement of both methods. Methods Three blinded human raters (neuroradiologist, neurologist, student) with different levels of clinical experience retrospectively assessed the Gray-White-Matter Ratio (GWR) in head CTs of 95 CA patients. GWR was also quantified by a recently published computer algorithm that uses coregistration with standardized brain spaces to identify regions of interest (ROIs). We calculated intraclass correlation (ICC) for inter-rater agreement between human and computer raters as well as area under the curve (AUC) and sensitivity/specificity for poor outcome prognostication. Results Inter-rater agreement on GWR was very good (ICC 0.82–0.84) between all three human raters across different levels of expertise and between the computer algorithm and neuroradiologist (ICC 0.83; 95% CI 0.78–0.88). Despite high overall agreement, we observed considerable, clinically relevant deviations of GWR measurements (up to 0.24) in individual patients. In our cohort, at a GWR threshold of 1.10, this did not lead to any false poor neurological outcome prediction. Conclusion Human and computer raters demonstrated high overall agreement in GWR determination in head CTs after CA. The clinically relevant deviations of GWR measurement in individual patients underscore the necessity of additional qualitative evaluation and integration of head CT findings into a multimodal approach to prognostication of neurological outcome after CA.
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Affiliation(s)
- Martin Kenda
- Department of Neurology With Experimental Neurology, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité—Universitätsmedizin Berlin, Berlin, Germany
- BIH Charité Junior Digital Clinician Scientist Program, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Berlin, Germany
- *Correspondence: Martin Kenda
| | - Zhuo Cheng
- Department of Neuroradiology, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité—Universitätsmedizin Berlin, Berlin, Germany
| | - Christopher Guettler
- Department of Neuroradiology, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité—Universitätsmedizin Berlin, Berlin, Germany
| | - Christian Storm
- Department of Nephrology and Intensive Care Medicine—Circulatory Arrest Center Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité—Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph J. Ploner
- Department of Neurology With Experimental Neurology, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité—Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph Leithner
- Department of Neurology With Experimental Neurology, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité—Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Scheel
- Department of Neuroradiology, Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité—Universitätsmedizin Berlin, Berlin, Germany
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16
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Lang M, Leithner C, Scheel M, Kenda M, Cronberg T, During J, Rylander C, Annborn M, Dankiewicz J, Deye N, Halliday T, Lascarrou JB, Matthew T, McGuigan P, Morgan M, Thomas M, Ullén S, Undén J, Nielsen N, Moseby-Knappe M. Prognostic accuracy of head computed tomography for prediction of functional outcome after out-of-hospital cardiac arrest: Rationale and design of the prospective TTM2-CT-substudy. Resusc Plus 2022; 12:100316. [PMID: 36267356 PMCID: PMC9576971 DOI: 10.1016/j.resplu.2022.100316] [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: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 11/30/2022] Open
Abstract
Background Head computed tomography (CT) is a guideline recommended method to predict functional outcome after cardiac arrest (CA), but standardized criteria for evaluation are lacking. To date, no prospective trial has systematically validated methods for diagnosing hypoxic-ischaemic encephalopathy (HIE) on CT after CA. We present a protocol for validation of pre-specified radiological criteria for assessment of HIE on CT for neuroprognostication after CA. Methods/design This is a prospective observational international multicentre substudy of the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Patients still unconscious 48 hours post-arrest at 13 participating hospitals were routinely examined with CT. Original images will be evaluated by examiners blinded to clinical data using a standardized protocol. Qualitative assessment will include evaluation of absence/presence of "severe HIE". Radiodensities will be quantified in pre-specified regions of interest for calculation of grey-white matter ratios (GWR) at the basal ganglia level. Functional outcome will be dichotomized into good (modified Rankin Scale 0-3) and poor (modified Rankin Scale 4-6) at six months post-arrest. Prognostic accuracies for good and poor outcome will be presented as sensitivities and specificities with 95% confidence intervals (using pre-specified cut-offs for quantitative analysis), descriptive statistics (Area Under the Receiver Operating Characteristics Curve), inter- and intra-rater reliabilities according to STARD guidelines. Conclusions The results from this prospective trial will validate a standardized approach to radiological evaluations of HIE on CT for prediction of functional outcome in comatose CA patients.The TTM2 trial and the TTM2 CT substudy are registered at ClinicalTrials.gov NCT02908308 and NCT03913065.
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Affiliation(s)
- Margareta Lang
- Department of Clinical Sciences Lund, Radiology, Lund University, Helsingborg Hospital, Helsingborg, Sweden,Corresponding author at: Helsingborg Hospital, Department of Radiology, 252 23 Helsingborg, Sweden.
| | - Christoph Leithner
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Germany
| | - Michael Scheel
- Department of Neuroradiology, Charité-Universitätsmedizin Berlin, Germany
| | - Martin Kenda
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Germany,Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Germany
| | - Tobias Cronberg
- Department of Clinical Sciences Lund, Neurology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Joachim During
- Department of Clinical Sciences Lund, Anaesthesia and Intensive Care, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Christian Rylander
- Department of Surgical Sciences, Anaesthesia and Intensive Care, Uppsala University, Uppsala, Sweden
| | - Martin Annborn
- Department of Clinical Sciences Lund, Anesthesia & Intensive Care, Lund University, Helsingborg Hospital, Helsingborg, Sweden
| | - Josef Dankiewicz
- Department of Clinical Sciences Lund, Cardiology, Lund University, Skåne University, Lund, Sweden
| | - Nicolas Deye
- Department of Medical and Toxicological Intensive Care Unit, Lariboisière Hospital, Paris, France
| | - Thomas Halliday
- Department of Operation and Intensive Care, Linköping University Hospital, Linköping, Sweden
| | | | - Thomas Matthew
- Intensive Care Unit, University Hospitals, Bristol and Weston, England, United Kingdom
| | - Peter McGuigan
- Regional Intensive Care Unit, Royal Victoria Hospital, Belfast, Northern Ireland, United Kingdom
| | - Matt Morgan
- Department of Intensive Care, the Royal Perth Hospital, Perth, Australia,Department of Intensive Care, The University Hospital of Wales, Cardiff, United Kingdom,School of Medicine, Curtin University, Perth, Australia
| | - Matthew Thomas
- University Hospitals, Bristol and Weston, United Kingdom
| | - Susann Ullén
- Clinical Studies Sweden – Forum South, Skåne University Hospital, Lund, Sweden
| | - Johan Undén
- Department of Clinical Science Lund, Lund, Sweden,Department of Operation and Intensive Care, Hallands Hospital Halmstad, Halmstad, Sweden
| | - Niklas Nielsen
- Department of Clinical Sciences Lund, Anesthesia & Intensive Care, Lund University, Helsingborg Hospital, Helsingborg, Sweden
| | - Marion Moseby-Knappe
- Department of Clinical Sciences Lund, Neurology, Lund University, Skåne University Hospital, Lund, Sweden
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17
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Lang M, Nielsen N, Ullén S, Abul-Kasim K, Johnsson M, Helbok R, Leithner C, Cronberg T, Moseby-Knappe M. A pilot study of methods for prediction of poor outcome by head computed tomography after cardiac arrest. Resuscitation 2022; 179:61-70. [PMID: 35931271 DOI: 10.1016/j.resuscitation.2022.07.035] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/18/2022] [Accepted: 07/27/2022] [Indexed: 10/16/2022]
Abstract
INTRODUCTION In Sweden, head computed tomography (CT) is commonly used for prediction of neurological outcome after cardiac arrest, as recommended by guidelines. We compare the prognostic ability and interrater variability of routine and novel CT methods for prediction of poor outcome. METHODS Retrospective study including patients from Swedish sites within the Target Temperature Management after out-of-hospital cardiac arrest trial examined with CT. Original images were assessed by two independent radiologists blinded from clinical data with eye-balling without pre-specified criteria, and with a semi-quantitative assessment. Grey-white-matter ratios (GWR) were quantified using models with 4-20 manually placed regions of interest. Prognostic abilities and interrater variability were calculated for prediction of poor outcome (modified Rankin Scale 4-6 at six months) for early (<24h) and late (≥24h) examinations. RESULTS 68/106 (64%) of included patients were examined <24h post-arrest. Eye-balling predicted poor outcome with 89-100% specificity and 15-78% sensitivity. GWR <24h predicted neurological outcome with unsatisfactory to satisfactory Area Under the Receiver Operating Characteristics Curve (AUROC: 0.54-0.64). GWR ≥24h yielded very good to excellent AUROC (0.80-0.93). Sensitivities increased >2-3 fold in examinations performed after 24h compared to early examinations. Combining eye-balling with GWR<1.15 predicted poor outcome without false positives with sensitivities remaining acceptable. CONCLUSION In our cohort, qualitative and quantitative CT methods predicted poor outcome with high specificity and low to moderate sensitivity. Sensitivity increased relevantly after the first 24 hours after CA. Interrater variability poses a problem and indicates the need to standardise brain CT evaluation to increase the methodś safety.
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Affiliation(s)
- Margareta Lang
- Department of Clinical Sciences Lund, Radiology, Lund University, Helsingborg Hospital, Lund, Sweden.
| | - Niklas Nielsen
- Department of Clinical Sciences Lund, Anaesthesia and Intensive Care, Lund University, Helsingborg Hospital, Lund, Sweden
| | - Susann Ullén
- Clinical Studies Sweden ‑ Forum South, Skåne University Hospital, Lund, Sweden
| | - Kasim Abul-Kasim
- Department of Clinical Sciences Lund, Radiology, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Mikael Johnsson
- Department of Radiology, Helsingborg Hospital, Helsingborg, Sweden
| | - Raimund Helbok
- Department of Neurology, Neurological Intensive Care Unit, Medical University Innsbruck, Innsbruck, Austria
| | - Christoph Leithner
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Germany
| | - Tobias Cronberg
- Department of Clinical Sciences Lund, Neurology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Marion Moseby-Knappe
- Department of Clinical Sciences Lund, Neurology, Lund University, Skåne University Hospital, Lund, Sweden
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18
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Beekman R, Crawford A, Mazurek MH, Prabhat AM, Chavva IR, Parasuram N, Kim N, Kim JA, Petersen N, de Havenon A, Khosla A, Honiden S, Miller PE, Wira C, Daley J, Payabvash S, Greer DM, Gilmore EJ, Taylor Kimberly W, Sheth KN. Bedside monitoring of hypoxic ischemic brain injury using low-field, portable brain magnetic resonance imaging after cardiac arrest. Resuscitation 2022; 176:150-158. [PMID: 35562094 PMCID: PMC9746653 DOI: 10.1016/j.resuscitation.2022.05.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Assessment of brain injury severity is critically important after survival from cardiac arrest (CA). Recent advances in low-field MRI technology have permitted the acquisition of clinically useful bedside brain imaging. Our objective was to deploy a novel approach for evaluating brain injury after CA in critically ill patients at high risk for adverse neurological outcome. METHODS This retrospective, single center study involved review of all consecutive portable MRIs performed as part of clinical care for CA patients between September 2020 and January 2022. Portable MR images were retrospectively reviewed by a blinded board-certified neuroradiologist (S.P.). Fluid-inversion recovery (FLAIR) signal intensities were measured in select regions of interest. RESULTS We performed 22 low-field MRI examinations in 19 patients resuscitated from CA (68.4% male, mean [standard deviation] age, 51.8 [13.1] years). Twelve patients (63.2%) had findings consistent with HIBI on conventional neuroimaging radiology report. Low-field MRI detected findings consistent with HIBI in all of these patients. Low-field MRI was acquired at a median (interquartile range) of 78 (40-136) hours post-arrest. Quantitatively, we measured FLAIR signal intensity in three regions of interest, which were higher amongst patients with confirmed HIBI. Low-field MRI was completed in all patients without disruption of intensive care unit equipment monitoring and no safety events occurred. CONCLUSION In a critically ill CA population in whom MR imaging is often not feasible, low-field MRI can be deployed at the bedside to identify HIBI. Low-field MRI provides an opportunity to evaluate the time-dependent nature of MRI findings in CA survivors.
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Affiliation(s)
- Rachel Beekman
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
| | - Anna Crawford
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Mercy H Mazurek
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Anjali M Prabhat
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Isha R Chavva
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Nethra Parasuram
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Noah Kim
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Jennifer A Kim
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Nils Petersen
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Akhil Khosla
- Department of Pulmonary Critical Care, Yale School of Medicine, New Haven, CT, USA
| | - Shyoko Honiden
- Department of Pulmonary Critical Care, Yale School of Medicine, New Haven, CT, USA
| | - P Elliott Miller
- Section of Cardiology, Yale School of Medicine, New Haven, CT, USA
| | - Charles Wira
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - James Daley
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | | | - David M Greer
- Department of Neurology, Boston University Medical Center, Boston, MA, USA
| | - Emily J Gilmore
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - W Taylor Kimberly
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
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19
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Hagberg G, Ihle-Hansen H, Sandset EC, Jacobsen D, Wimmer H, Ihle-Hansen H. Long Term Cognitive Function After Cardiac Arrest: A Mini-Review. Front Aging Neurosci 2022; 14:885226. [PMID: 35721022 PMCID: PMC9204346 DOI: 10.3389/fnagi.2022.885226] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality worldwide. With better pre- and inhospital treatment, including cardiopulmonary resuscitation (CPR) as an integrated part of public education and more public-access defibrillators available, OHCA survival has increased over the last decade. There are concerns, after successful resuscitation, of cerebral hypoxia and degrees of potential acquired brain injury with resulting poor cognitive functioning. Cognitive function is not routinely assessed in OHCA survivors, and there is a lack of consensus on screening methods for cognitive changes. This narrative mini-review, explores available evidence on hypoxic brain injury and long-term cognitive function in cardiac arrest survivors and highlights remaining knowledge deficits.
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20
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External validation of the 2020 ERC/ESICM prognostication strategy algorithm after cardiac arrest. Crit Care 2022; 26:95. [PMID: 35399085 PMCID: PMC8996564 DOI: 10.1186/s13054-022-03954-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 03/18/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Purpose
To assess the performance of the post-cardiac arrest (CA) prognostication strategy algorithm recommended by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) in 2020.
Methods
This was a retrospective analysis of the Korean Hypothermia Network Prospective Registry 1.0. Unconscious patients without confounders at day 4 (72–96 h) after return of spontaneous circulation (ROSC) were included. The association between the prognostic factors included in the prognostication strategy algorithm, except status myoclonus and the neurological outcome, was investigated, and finally, the prognostic performance of the prognostication strategy algorithm was evaluated. Poor outcome was defined as cerebral performance categories 3–5 at 6 months after ROSC.
Results
A total of 660 patients were included in the final analysis. Of those, 108 (16.4%) patients had a good neurological outcome at 6 months after CA. The 2020 ERC/ESICM prognostication strategy algorithm identified patients with poor neurological outcome with 60.2% sensitivity (95% CI 55.9–64.4) and 100% specificity (95% CI 93.9–100) among patients who were unconscious or had a GCS_M score ≤ 3 and with 58.2% sensitivity (95% CI 53.9–62.3) and 100% specificity (95% CI 96.6–100) among unconscious patients. When two prognostic factors were combined, any combination of prognostic factors had a false positive rate (FPR) of 0 (95% CI 0–5.6 for combination of no PR/CR and poor CT, 0–30.8 for combination of No SSEP N20 and NSE 60).
Conclusion
The 2020 ERC/ESICM prognostication strategy algorithm predicted poor outcome without an FPR and with sensitivities of 58.2–60.2%. Any combinations of two predictors recommended by ERC/ESICM showed 0% of FPR.
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21
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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: 7] [Impact Index Per Article: 3.5] [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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22
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Nam In Y, Ho Lee I, Soo Park J, Mi Kim Data Acquisition D, You Data Acquisition Y, Hong Min J, Jeong W, Jun Ahn H, Kang C, Kook Lee B. Delayed head CT in out-of-hospital cardiac arrest survivors: Does this improve predictive performance of neurological outcome? Resuscitation 2022; 172:1-8. [PMID: 35026330 DOI: 10.1016/j.resuscitation.2022.01.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 01/04/2022] [Accepted: 01/04/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND We compared the ability of head computed tomography (HCT) and MRI, respectively, obtained before or after target temperature management to predict neurologic outcomes in out-of-hospital cardiac arrest (OHCA) survivors. METHODS This retrospective study included adult comatose OHCA survivors who underwent neuroimaging scans within 6 h (first HCT) or 72-96 h (second HCT and MRI) after the return of spontaneous circulation (ROSC). We calculated the gray-white matter ratio (GWR), hypoxic-ischemic brain injury presence (loss of boundary at the basal ganglia level [LOB at BG], sulcal effacement at the centrum semiovale [SE at CS], and pseudo-SAH sign), and the overall score based on MRI findings (a total score of 21 brain regions individually scored according to the degree of signal abnormality). RESULTS Overall, 78 patients were included in this analysis, of whom 45 (58%) showed poor outcomes. The second HCT scan showed greater prognostic performance than the first HCT scan for GWR (area under curve 0.92 vs. 0.70), LOB at BG (0.93 vs. 0.65), SE at CS (0.89 vs. 0.64), and pseudo-SAH sign (0.75 vs. 0.51). The overall score on MRI (0.99) showed the highest prognostic performance. However, on the second HCT scan, the combination of GWR and LOB at BG showed prognostic performance (0.96) comparable to the overall score on MRI (P=0.12); the corresponding sensitivity and specificity values were 85.7% and 100%. CONCLUSIONS Overall score on MRI and the combination of GWR and LOB at BG findings on second HCT scans may help predict poor outcomes in OHCA survivors.
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Affiliation(s)
- Yong Nam In
- Department of Emergency Medicine, College of Medicine, Chungnam National University, Daejeon, Republic of Korea; Department of Emergency Medicine, Sejong Chungnam National University Hospital, Daejoen, Republic of Korea
| | - In Ho Lee
- Department of Radiology, College of Medicine, Chungnam National University, 266, Munhwa-ro, Jung-gu, Daejeon, Republic of Korea
| | - Jung Soo Park
- Department of Emergency Medicine, College of Medicine, Chungnam National University, Daejeon, Republic of Korea; Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea.
| | - Da Mi Kim Data Acquisition
- Department of Radiology, College of Medicine, Chungnam National University, 266, Munhwa-ro, Jung-gu, Daejeon, Republic of Korea
| | | | - Jin Hong Min
- Department of Emergency Medicine, College of Medicine, Chungnam National University, Daejeon, Republic of Korea; Department of Emergency Medicine, Sejong Chungnam National University Hospital, Daejoen, Republic of Korea
| | - Wonjoon Jeong
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
| | - Hong Jun Ahn
- Department of Emergency Medicine, College of Medicine, Chungnam National University, Daejeon, Republic of Korea; Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
| | - Changshin Kang
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
| | - Byung Kook Lee
- Department of Emergency Medicine, Chonnam national University Medical School, Chonnam National Univesity Hospital, Gwangju, Republic of Korea
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Kirsch K, Heymel S, Günther A, Vahl K, Schmidt T, Michalski D, Fritzenwanger M, Schulze PC, Pfeifer R. Prognostication of neurologic outcome using gray-white-matter-ratio in comatose patients after cardiac arrest. BMC Neurol 2021; 21:456. [PMID: 34809608 PMCID: PMC8607613 DOI: 10.1186/s12883-021-02480-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 11/05/2021] [Indexed: 01/14/2023] Open
Abstract
Background This study aimed to assess the prognostic value regarding neurologic outcome of CT neuroimaging based Gray-White-Matter-Ratio measurement in patients after resuscitation from cardiac arrest. Methods We retrospectively evaluated CT neuroimaging studies of 91 comatose patients resuscitated from cardiac arrest and 46 non-comatose controls. We tested the diagnostic performance of Gray-White-Matter-Ratio compared with established morphologic signs of hypoxic-ischaemic brain injury, e. g. loss of distinction between gray and white matter, and laboratory parameters, i. e. neuron-specific enolase, for the prediction of poor neurologic outcomes after resuscitated cardiac arrest. Primary endpoint was neurologic function assessed with cerebral performance category score 30 days after the index event. Results Gray-White-Matter-Ratio showed encouraging interobserver variability (ICC 0.670 [95% CI: 0.592–0.741] compared to assessment of established morphologic signs of hypoxic-ischaemic brain injury (Fleiss kappa 0.389 [95% CI: 0.320–0.457]) in CT neuroimaging studies. It correlated with cerebral performance category score with lower Gray-White-Matter-Ratios associated with unfavourable neurologic outcomes. A cut-off of 1.17 derived from the control population predicted unfavourable neurologic outcomes in adult survivors of cardiac arrest with 100% specificity, 50.3% sensitivity, 100% positive predictive value, and 39.3% negative predictive value. Gray-White-Matter-Ratio prognostic power depended on the time interval between circulatory arrest and CT imaging, with increasing sensitivity the later the image acquisition was executed. Conclusions A reduced Gray-White-Matter-Ratio is a highly specific prognostic marker of poor neurologic outcomes early after resuscitation from cardiac arrest. Sensitivity seems to be dependent on the time interval between circulatory arrest and image acquisition, with limited value within the first 12 h.
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Affiliation(s)
- Konrad Kirsch
- Department of Internal Medicine I, Jena University Hospital, Am Klinikum 1, 07747, Jena, Germany.
| | - Stefan Heymel
- Department of Internal Medicine I, Division of Medical Intensive Care, Jena University Hospital, Am Klinikum 1, 07747, Jena, Germany
| | - Albrecht Günther
- Department of Neurology, Jena University Hospital, Am Klinikum 1, 07747, Jena, Germany
| | - Kathleen Vahl
- Department of Radiology, Interventional Radiology and Neuroradiology, Klinikum Altenburger Land, Am Waldessaum 10, 04600, Altenburg, Germany
| | - Thorsten Schmidt
- Department of Diagnostic and Interventional Neuroradiology, HELIOS Klinikum Wuppertal, Heusnerstraße 40, 42283, Wuppertal, Germany
| | - Dominik Michalski
- Department of Neurology, University Hospital Leipzig, Liebigstraße 20, 04103, Leipzig, Germany
| | - Michael Fritzenwanger
- Department of Internal Medicine I, Division of Medical Intensive Care, Jena University Hospital, Am Klinikum 1, 07747, Jena, Germany
| | - Paul Christian Schulze
- Department of Internal Medicine I, Jena University Hospital, Am Klinikum 1, 07747, Jena, Germany
| | - Rüdiger Pfeifer
- Department of Internal Medicine I, Division of Medical Intensive Care, Jena University Hospital, Am Klinikum 1, 07747, Jena, Germany
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Short-Burchell RJ, Corke CF, Carne RP, Orford NR, Maiden MJ. Documentation of neurological status in patients admitted to an intensive care unit after cardiac arrest: A 10-year cohort study. Aust Crit Care 2021; 35:557-563. [PMID: 34711494 DOI: 10.1016/j.aucc.2021.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 08/16/2021] [Accepted: 08/17/2021] [Indexed: 10/20/2022] Open
Abstract
OBJECTIVE The objective of this study was to describe the documented neurological assessment and investigations for neuroprognostication in patients after cardiac arrest. DESIGN, SETTING, AND PARTICIPANTS This was a retrospective cohort study of adult patients after cardiac arrest, admitted to a tertiary intensive care unit (ICU), between January 2009 and December 2018. MAIN OUTCOME MEASURES The main outcome measures were the proportion of patients with a documented Glasgow Coma Scale (GCS) score and investigations for neuroprognostication. RESULTS Four hundred twenty-seven patients formed the study cohort. The GCS score was documented for 267 (63%) patients at some time during their ICU stay. The proportion of patients with the GCS score documented decreased each day of ICU stay (59% at day 1, 20% at day 5). Pupil reflex to light was recorded in 352 (82%), corneal reflex in 155 (36%), and limb reflexes in 216 (51%) patients. Twenty-eight (6.6%) patients underwent brain magnetic resonance imaging, 10 (2.3%) an electroencephalogram, and two somatosensory evoked potentials. Withdrawal of life-sustaining treatments occurred in 166 (39%) patients, and 221 (52%) patients died in hospital. CONCLUSIONS In this single-centre study of patients admitted to the ICU after cardiac arrest, the GCS score was inconsistently documented, and investigations for neuroprognostication were infrequent.
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Affiliation(s)
- Robert J Short-Burchell
- Intensive Care Unit, University Hospital Geelong, Barwon Health, Geelong, Victoria, Australia.
| | - Charles F Corke
- Intensive Care Unit, University Hospital Geelong, Barwon Health, Geelong, Victoria, Australia; School of Medicine, Deakin University, Victoria, Australia
| | - Ross P Carne
- Department of Neurosciences, University Hospital Geelong, Barwon Health, Geelong, Victoria, Australia; School of Medicine, Deakin University, Victoria, Australia
| | - Neil R Orford
- Intensive Care Unit, University Hospital Geelong, Barwon Health, Geelong, Victoria, Australia; School of Medicine, Deakin University, Victoria, Australia
| | - Matthew J Maiden
- Intensive Care Unit, University Hospital Geelong, Barwon Health, Geelong, Victoria, Australia; Intensive Care Unit, Royal Adelaide Hospital, Adelaide, South Australia, Australia
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Perkins GD, Callaway CW, Haywood K, Neumar RW, Lilja G, Rowland MJ, Sawyer KN, Skrifvars MB, Nolan JP. Brain injury after cardiac arrest. Lancet 2021; 398:1269-1278. [PMID: 34454687 DOI: 10.1016/s0140-6736(21)00953-3] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 04/20/2021] [Accepted: 04/20/2021] [Indexed: 12/16/2022]
Abstract
As more people are surviving cardiac arrest, focus needs to shift towards improving neurological outcomes and quality of life in survivors. Brain injury after resuscitation, a common sequela following cardiac arrest, ranges in severity from mild impairment to devastating brain injury and brainstem death. Effective strategies to minimise brain injury after resuscitation include early intervention with cardiopulmonary resuscitation and defibrillation, restoration of normal physiology, and targeted temperature management. It is important to identify people who might have a poor outcome, to enable informed choices about continuation or withdrawal of life-sustaining treatments. Multimodal prediction guidelines seek to avoid premature withdrawal in those who might survive with a good neurological outcome, or prolonging treatment that might result in survival with severe disability. Approximately one in three admitted to intensive care will survive, many of whom will need intensive, tailored rehabilitation after discharge to have the best outcomes.
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Affiliation(s)
- Gavin D Perkins
- Warwick Medical School, University of Warwick, Coventry, UK; Critical Care Unit, University Hospitals Birmingham, Birmingham, UK.
| | - Clifton W Callaway
- University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Robert W Neumar
- Department of Emergency Medicine, Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, USA
| | - Gisela Lilja
- Neurology, Department of Clinical Sciences Lund, Lund University, Skane University Hospital, Lund, Sweden
| | - Matthew J Rowland
- Kadoorie Centre for Critical Care Research, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Kelly N Sawyer
- University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Markus B Skrifvars
- Department of Emergency Care and Services, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Jerry P Nolan
- Warwick Medical School, University of Warwick, Coventry, UK; Anaesthesia and Intensive Care Medicine, Royal United Hospital, Bath, UK
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26
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Wiklund L, Sharma A, Patnaik R, Muresanu DF, Sahib S, Tian ZR, Castellani RJ, Nozari A, Lafuente JV, Sharma HS. Upregulation of hemeoxygenase enzymes HO-1 and HO-2 following ischemia-reperfusion injury in connection with experimental cardiac arrest and cardiopulmonary resuscitation: Neuroprotective effects of methylene blue. PROGRESS IN BRAIN RESEARCH 2021; 265:317-375. [PMID: 34560924 DOI: 10.1016/bs.pbr.2021.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Oxidative stress plays an important role in neuronal injuries after cardiac arrest. Increased production of carbon monoxide (CO) by the enzyme hemeoxygenase (HO) in the brain is induced by the oxidative stress. HO is present in the CNS in two isoforms, namely the inducible HO-1 and the constitutive HO-2. Elevated levels of serum HO-1 occurs in cardiac arrest patients and upregulation of HO-1 in cardiac arrest is seen in the neurons. However, the role of HO-2 in cardiac arrest is not well known. In this review involvement of HO-1 and HO-2 enzymes in the porcine brain following cardiac arrest and resuscitation is discussed based on our own observations. In addition, neuroprotective role of methylene blue- an antioxidant dye on alterations in HO under in cardiac arrest is also presented. The biochemical findings of HO-1 and HO-2 enzymes using ELISA were further confirmed by immunocytochemical approach to localize selective regional alterations in cardiac arrest. Our observations are the first to show that cardiac arrest followed by successful cardiopulmonary resuscitation results in significant alteration in cerebral concentrations of HO-1 and HO-2 levels indicating a prominent role of CO in brain pathology and methylene blue during CPR followed by induced hypothermia leading to superior neuroprotection after return of spontaneous circulation (ROSC), not reported earlier.
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Affiliation(s)
- Lars Wiklund
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Department of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden
| | - Aruna Sharma
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Department of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden.
| | - Ranjana Patnaik
- Department of Biomaterials, School of Biomedical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, India
| | - Dafin F Muresanu
- Department of Clinical Neurosciences, University of Medicine & Pharmacy, Cluj-Napoca, Romania; "RoNeuro" Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Seaab Sahib
- Department of Chemistry & Biochemistry, University of Arkansas, Fayetteville, AR, United States
| | - Z Ryan Tian
- Department of Chemistry & Biochemistry, University of Arkansas, Fayetteville, AR, United States
| | - Rudy J Castellani
- Department of Pathology, University of Maryland, Baltimore, MD, United States
| | - Ala Nozari
- Anesthesiology & Intensive Care, Massachusetts General Hospital, Boston, MA, United States
| | - José Vicente Lafuente
- LaNCE, Department of Neuroscience, University of the Basque Country (UPV/EHU), Leioa, Bizkaia, Spain
| | - Hari Shanker Sharma
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Department of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden.
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Automated Assessment of Brain CT After Cardiac Arrest-An Observational Derivation/Validation Cohort Study. Crit Care Med 2021; 49:e1212-e1222. [PMID: 34374503 DOI: 10.1097/ccm.0000000000005198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Objectives Prognostication of outcome is an essential step in defining therapeutic goals after cardiac arrest. Gray-white-matter ratio obtained from brain CT can predict poor outcome. However, manual placement of regions of interest is a potential source of error and interrater variability. Our objective was to assess the performance of poor outcome prediction by automated quantification of changes in brain CTs after cardiac arrest. Design Observational, derivation/validation cohort study design. Outcome was determined using the Cerebral Performance Category upon hospital discharge. Poor outcome was defined as death or unresponsive wakefulness syndrome/coma. CTs were automatically decomposed using coregistration with a brain atlas. Setting ICUs at a large, academic hospital with circulatory arrest center. Patients We identified 433 cardiac arrest patients from a large previously established database with brain CTs within 10 days after cardiac arrest. Interventions None. Measurements and Main Results Five hundred sixteen brain CTs were evaluated (derivation cohort n = 309, validation cohort n = 207). Patients with poor outcome had significantly lower radiodensities in gray matter regions. Automated GWR_si (putamen/posterior limb of internal capsule) was performed with an area under the curve of 0.86 (95%-CI: 0.80-0.93) for CTs taken later than 24 hours after cardiac arrest (similar performance in the validation cohort). Poor outcome (Cerebral Performance Category 4-5) was predicted with a specificity of 100% (95% CI, 87-100%, derivation; 88-100%, validation) at a threshold of less than 1.10 and a sensitivity of 49% (95% CI, 36-58%, derivation) and 38% (95% CI, 27-50%, validation) for CTs later than 24 hours after cardiac arrest. Sensitivity and area under the curve were lower for CTs performed within 24 hours after cardiac arrest. Conclusions Automated gray-white-matter ratio from brain CT is a promising tool for prediction of poor neurologic outcome after cardiac arrest with high specificity and low-to-moderate sensitivity. Prediction by gray-white-matter ratio at the basal ganglia level performed best. Sensitivity increased considerably for CTs performed later than 24 hours after cardiac arrest.
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Abstract
PURPOSE OF REVIEW Recovery after severe brain injury is variable and challenging to accurately predict at the individual patient level. This review highlights new developments in clinical prognostication with a special focus on the prediction of consciousness and increasing reliance on methods from data science. RECENT FINDINGS Recent research has leveraged serum biomarkers, quantitative electroencephalography, MRI, and physiological time-series to build models for recovery prediction. The analysis of high-resolution data and the integration of features from different modalities can be approached with efficient computational techniques. SUMMARY Advances in neurophysiology and neuroimaging, in combination with computational methods, represent a novel paradigm for prediction of consciousness and functional recovery after severe brain injury. Research is needed to produce reliable, patient-level predictions that could meaningfully impact clinical decision making.
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Prognostic Values of the Gray-to-White Matter Ratio on Brain Computed Tomography Images for Neurological Outcomes after Cardiac Arrest: A Meta-Analysis. BIOMED RESEARCH INTERNATIONAL 2021; 2020:7949516. [PMID: 33490256 PMCID: PMC7803139 DOI: 10.1155/2020/7949516] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/07/2020] [Accepted: 10/22/2020] [Indexed: 12/30/2022]
Abstract
Materials and Methods The PubMed, ScienceDirect, Web of Science, and China National Knowledge Infrastructure databases were searched for all relevant articles published before March 31, 2020, without any language restrictions. The pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated with a random-effects model using Stata 14.0 software. Result A total of 24 eligible studies with 2812 CA patients were recruited in the meta-analysis. The pooled result showed that decreased GWR was correlated with poor neurological outcomes after CA (OR = 11.28, 95% CI: 6.29–20.21, and P < 0.001) with moderate heterogeneity (I2 = 71.5%, P < 0.001). The pooled sensitivity and specificity were 0.58 (95% CI: 0.47–0.68) and 0.95 (95% CI: 0.87–0.98), respectively. The area under the curve (AUC) of GWR was 0.84 (95% CI: 0.80–0.87). Compared with GWR (cerebrum) and GWR (average), GWR using the basal ganglion level of brain CT had the highest AUC of 0.87 (0.84–0.90). Subgroup analysis indicated that heterogeneity may be derived from the time of CT measurement, preset specificity, targeted temperature management, or proportion of cardiac etiology. Sensitivity analysis indicated that the result was stable, and Deeks' plot showed no possible publication bias (P = 0 .64). Conclusion Current research suggests that GWR, especially using the basal ganglion level of brain CT, is a useful parameter for determining neurological outcomes after CA.
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30
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Abstract
Cardiac arrest is a catastrophic event with high morbidity and mortality. Despite advances over time in cardiac arrest management and postresuscitation care, the neurologic consequences of cardiac arrest are frequently devastating to patients and their families. Targeted temperature management is an intervention aimed at limiting postanoxic injury and improving neurologic outcomes following cardiac arrest. Recovery of neurologic function governs long-term outcome after cardiac arrest and prognosticating on the potential for recovery is a heavy burden for physicians. An early and accurate estimate of the potential for recovery can establish realistic expectations and avoid futile care in those destined for a poor outcome. This chapter reviews the epidemiology, pathophysiology, therapeutic interventions, prognostication, and neurologic sequelae of cardiac arrest.
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Affiliation(s)
- Rick Gill
- Department of Neurology, Loyola University Chicago, Chicago, Stritch School of Medicine, Maywood, IL, United States
| | - Michael Teitcher
- Department of Neurology, Loyola University Chicago, Chicago, Stritch School of Medicine, Maywood, IL, United States
| | - Sean Ruland
- Department of Neurology, Loyola University Chicago, Chicago, Stritch School of Medicine, Maywood, IL, United States.
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31
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Kirschen MP, Licht DJ, Faerber J, Mondal A, Graham K, Winters M, Balu R, Diaz-Arrastia R, Berg RA, Topjian A, Vossough A. Association of MRI Brain Injury With Outcome After Pediatric Out-of-Hospital Cardiac Arrest. Neurology 2020; 96:e719-e731. [PMID: 33208547 DOI: 10.1212/wnl.0000000000011217] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 09/23/2020] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE To determine the association between the extent of diffusion restriction and T2/fluid-attenuated inversion recovery (FLAIR) injury on brain MRI and outcomes after pediatric out-of-hospital cardiac arrest (OHCA). METHODS Diffusion restriction and T2/FLAIR injury were described according to the pediatric MRI modification of the Alberta Stroke Program Early Computed Tomography Score (modsASPECTS) for children from 2005 to 2013 who had an MRI within 14 days of OHCA. The primary outcome was unfavorable neurologic outcome defined as ≥1 change in Pediatric Cerebral Performance Category (PCPC) from baseline resulting in a hospital discharge PCPC score 3, 4, 5, or 6. Patients with unfavorable outcomes were further categorized into alive with PCPC 3-5, dead due to withdrawal of life-sustaining therapies for poor neurologic prognosis (WLST-neuro), or dead by neurologic criteria. RESULTS We evaluated MRI scans from 77 patients (median age 2.21 [interquartile range 0.44, 13.07] years) performed 4 (2, 6) days postarrest. Patients with unfavorable outcomes had more extensive diffusion restriction (median 7 [4, 10.3] vs 0 [0, 0] regions, p < 0.001) and T2/FLAIR injury (5.5 [2.3, 8.2] vs 0 [0, 0.75] regions, p < 0.001) compared to patients with favorable outcomes. Area under the receiver operating characteristic curve for the extent of diffusion restriction and unfavorable outcome was 0.96 (95% confidence interval [CI] 0.91, 0.99) and 0.92 (95% CI 0.85, 0.97) for T2/FLAIR injury. There was no difference in extent of diffusion restriction between patients who were alive with an unfavorable outcome and patients who died from WLST-neuro (p = 0.11). CONCLUSIONS More extensive diffusion restriction and T2/FLAIR injury on the modsASPECTS score within the first 14 days after pediatric cardiac arrest was associated with unfavorable outcomes at hospital discharge.
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Affiliation(s)
- Matthew P Kirschen
- From the Department of Anesthesiology and Critical Care Medicine (M.P.K., K.G., M.W., R.A.B., A.T.), Department of Pediatrics (M.P.K., D.J.L., R.A.B., A.T.), Health Analytics Unit (J.F., A.M.), and Department of Radiology (A.V.), Children's Hospital of Philadelphia; and Department of Neurology (M.P.K., D.J.L., R.B., R.D.-A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia.
| | - Daniel J Licht
- From the Department of Anesthesiology and Critical Care Medicine (M.P.K., K.G., M.W., R.A.B., A.T.), Department of Pediatrics (M.P.K., D.J.L., R.A.B., A.T.), Health Analytics Unit (J.F., A.M.), and Department of Radiology (A.V.), Children's Hospital of Philadelphia; and Department of Neurology (M.P.K., D.J.L., R.B., R.D.-A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Jennifer Faerber
- From the Department of Anesthesiology and Critical Care Medicine (M.P.K., K.G., M.W., R.A.B., A.T.), Department of Pediatrics (M.P.K., D.J.L., R.A.B., A.T.), Health Analytics Unit (J.F., A.M.), and Department of Radiology (A.V.), Children's Hospital of Philadelphia; and Department of Neurology (M.P.K., D.J.L., R.B., R.D.-A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Antara Mondal
- From the Department of Anesthesiology and Critical Care Medicine (M.P.K., K.G., M.W., R.A.B., A.T.), Department of Pediatrics (M.P.K., D.J.L., R.A.B., A.T.), Health Analytics Unit (J.F., A.M.), and Department of Radiology (A.V.), Children's Hospital of Philadelphia; and Department of Neurology (M.P.K., D.J.L., R.B., R.D.-A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Kathryn Graham
- From the Department of Anesthesiology and Critical Care Medicine (M.P.K., K.G., M.W., R.A.B., A.T.), Department of Pediatrics (M.P.K., D.J.L., R.A.B., A.T.), Health Analytics Unit (J.F., A.M.), and Department of Radiology (A.V.), Children's Hospital of Philadelphia; and Department of Neurology (M.P.K., D.J.L., R.B., R.D.-A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Madeline Winters
- From the Department of Anesthesiology and Critical Care Medicine (M.P.K., K.G., M.W., R.A.B., A.T.), Department of Pediatrics (M.P.K., D.J.L., R.A.B., A.T.), Health Analytics Unit (J.F., A.M.), and Department of Radiology (A.V.), Children's Hospital of Philadelphia; and Department of Neurology (M.P.K., D.J.L., R.B., R.D.-A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Ramani Balu
- From the Department of Anesthesiology and Critical Care Medicine (M.P.K., K.G., M.W., R.A.B., A.T.), Department of Pediatrics (M.P.K., D.J.L., R.A.B., A.T.), Health Analytics Unit (J.F., A.M.), and Department of Radiology (A.V.), Children's Hospital of Philadelphia; and Department of Neurology (M.P.K., D.J.L., R.B., R.D.-A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Ramon Diaz-Arrastia
- From the Department of Anesthesiology and Critical Care Medicine (M.P.K., K.G., M.W., R.A.B., A.T.), Department of Pediatrics (M.P.K., D.J.L., R.A.B., A.T.), Health Analytics Unit (J.F., A.M.), and Department of Radiology (A.V.), Children's Hospital of Philadelphia; and Department of Neurology (M.P.K., D.J.L., R.B., R.D.-A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Robert A Berg
- From the Department of Anesthesiology and Critical Care Medicine (M.P.K., K.G., M.W., R.A.B., A.T.), Department of Pediatrics (M.P.K., D.J.L., R.A.B., A.T.), Health Analytics Unit (J.F., A.M.), and Department of Radiology (A.V.), Children's Hospital of Philadelphia; and Department of Neurology (M.P.K., D.J.L., R.B., R.D.-A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Alexis Topjian
- From the Department of Anesthesiology and Critical Care Medicine (M.P.K., K.G., M.W., R.A.B., A.T.), Department of Pediatrics (M.P.K., D.J.L., R.A.B., A.T.), Health Analytics Unit (J.F., A.M.), and Department of Radiology (A.V.), Children's Hospital of Philadelphia; and Department of Neurology (M.P.K., D.J.L., R.B., R.D.-A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Arastoo Vossough
- From the Department of Anesthesiology and Critical Care Medicine (M.P.K., K.G., M.W., R.A.B., A.T.), Department of Pediatrics (M.P.K., D.J.L., R.A.B., A.T.), Health Analytics Unit (J.F., A.M.), and Department of Radiology (A.V.), Children's Hospital of Philadelphia; and Department of Neurology (M.P.K., D.J.L., R.B., R.D.-A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia
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Cardiac arrest: An interdisciplinary scoping review of the literature from 2019. Resusc Plus 2020; 4:100037. [PMID: 34223314 PMCID: PMC8244427 DOI: 10.1016/j.resplu.2020.100037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/29/2020] [Accepted: 10/04/2020] [Indexed: 01/09/2023] Open
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. Now in its second year, the goals of the review are to illustrate best practices in research and help reduce compartmentalization of knowledge by disseminating clinically relevant advances in the field of cardiac arrest across disciplines. Methods An electronic search of PubMed using keywords related to cardiac arrest was conducted. Title and abstracts retrieved by these searches were screened for relevance, classified by article type (original research or review), and sorted into 7 categories. Screened manuscripts underwent standardized scoring of overall methodological quality and impact on the categorized fields of study by reviewer teams lead by a subject-matter expert editor. Articles scoring higher than 99 percentiles by category-type were selected for full critique. Systematic differences between editors’ and reviewers’ scores were assessed using Wilcoxon signed-rank test. Results A total of 3348 articles were identified on initial search; of these, 1364 were scored after screening for relevance and deduplication, and forty-five underwent full critique. Epidemiology & Public Health represented 24% of fully reviewed articles with Prehospital Resuscitation, Technology & Care, and In-Hospital Resuscitation & Post-Arrest Care Categories both representing 20% of fully reviewed articles. There were no significant differences between editor and reviewer scoring. Conclusions The sheer number of articles screened is a testament to the need for an accessible source calling attention to high-quality and impactful research and serving as a high-yield reference for clinicians and scientists seeking to follow the ever-growing body of cardiac arrest-related literature. This will promote further development of the unique and interdisciplinary field of cardiac arrest medicine.
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Lupton JR, Kurz MC, Daya MR. Neurologic prognostication after resuscitation from cardiac arrest. J Am Coll Emerg Physicians Open 2020; 1:333-341. [PMID: 33000056 PMCID: PMC7493528 DOI: 10.1002/emp2.12109] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/28/2020] [Accepted: 05/01/2020] [Indexed: 12/11/2022] Open
Abstract
Out-of-hospital cardiac arrest remains a leading cause of mortality in the United States, and the majority of patients who die after achieving return of spontaneous circulation die from withdrawal of care due to a perceived poor neurologic prognosis. Unfortunately, withdrawal of care often occurs during the first day of admission and research suggests this early withdrawal of care may be premature and result in unnecessary deaths for patients who would have made a full neurologic recovery. In this review, we explore the evidence for neurologic prognostication in the emergency department for patients who achieve return of spontaneous circulation after an out-of-hospital cardiac arrest.
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Affiliation(s)
| | | | - Mohamud R Daya
- Oregon Health and Science University Portland Oregon USA
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Coppler PJ, Callaway CW, Guyette FX, Baldwin M, Elmer J. Early risk stratification after resuscitation from cardiac arrest. J Am Coll Emerg Physicians Open 2020; 1:922-931. [PMID: 33145541 PMCID: PMC7593432 DOI: 10.1002/emp2.12043] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 02/12/2020] [Accepted: 02/18/2020] [Indexed: 01/08/2023] Open
Abstract
Emergency clinicians often resuscitate cardiac arrest patients, and after acute resuscitation, clinicians face multiple decisions regarding disposition. Recent evidence suggests that out-of-hospital cardiac arrest patients with return of spontaneous circulation have higher odds of survival to hospital discharge, long-term survival, and improved functional outcomes when treated at centers that can provide advanced multidisciplinary care. For community clinicians, a high volume cardiac arrest center may be hours away. While current guidelines recommend against neurological prognostication in the first hours or days after return of spontaneous circulation, there are early findings suggestive of irrecoverable brain injury in which the patient would receive no benefit from transfer. In this Concepts article, we describe a simplified approach to quickly evaluate neurological status in cardiac arrest patients and identify findings concerning for irrecoverable brain injury. Characteristics of the arrest and resuscitation, initial neurological assessment, and brain computed tomography together can identify patients with high likelihood of irrecoverable anoxic injury. Patients who may benefit from centers with access to continuous electroencephalography are discussed. This approach can be used to identify patients who may benefit from rapid transfer to cardiac arrest centers versus those who may benefit from care close to home. Risk stratification also can provide realistic expectations for recovery to families.
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Affiliation(s)
- Patrick J. Coppler
- Department of Emergency MedicineUniversity of Pittsburgh School of MedicinePittsburghPennsylvania
| | - Clifton W. Callaway
- Department of Emergency MedicineUniversity of Pittsburgh School of MedicinePittsburghPennsylvania
| | - Francis X. Guyette
- Department of Emergency MedicineUniversity of Pittsburgh School of MedicinePittsburghPennsylvania
| | - Maria Baldwin
- Department of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPennsylvania
| | - Jonathan Elmer
- Department of Emergency MedicineUniversity of Pittsburgh School of MedicinePittsburghPennsylvania
- Department of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPennsylvania
- Department of Critical Care MedicineUniversity of Pittsburgh School of MedicinePittsburghPennsylvania
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Cronberg T, Greer DM, Lilja G, Moulaert V, Swindell P, Rossetti AO. Brain injury after cardiac arrest: from prognostication of comatose patients to rehabilitation. Lancet Neurol 2020; 19:611-622. [PMID: 32562686 DOI: 10.1016/s1474-4422(20)30117-4] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 03/30/2020] [Accepted: 04/01/2020] [Indexed: 02/08/2023]
Abstract
More patients are surviving cardiac arrest than ever before; however, the burden now lies with estimating neurological prognoses in a large number of patients who were initially comatose, in whom the ultimate outcome is unclear. Neurologists, neurointensivists, and clinical neurophysiologists must accurately balance the concern that overly conservative prognostication could leave patients in a severely disabled state, with the possibility that inaccurately pessimistic prognostication could lead to the withdrawal of life-sustaining treatment in patients who might otherwise have a good functional outcome. Prognostic tools have improved greatly, including electrophysiological tests, neuroimaging, and chemical biomarkers. Conclusions about the prognosis should be delayed at least 72 h after arrest to allow for the clearance of sedative drugs. Cognitive impairments, emotional problems, and fatigue are common among patients who have survived cardiac arrest, and often go unrecognised despite being related to caregiver burden and a decreased participation in society. Through simple screening, these problems can be identified, and patients can be provided with adequate information and rehabilitation.
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Affiliation(s)
- Tobias Cronberg
- Department of Clinical Sciences, Neurology, Lund University, Skane University Hospital, Lund, Sweden.
| | - David M Greer
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Gisela Lilja
- Department of Clinical Sciences, Neurology, Lund University, Skane University Hospital, Lund, Sweden
| | - Véronique Moulaert
- Department of Rehabilitation Medicine, University of Groningen, University Medical Centre Groningen, Netherlands
| | | | - Andrea O Rossetti
- Department of Clinical Neurosciences, University Hospital and University of Lausanne, Lausanne, Switzerland
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Smith AE, Friess SH. Neurological Prognostication in Children After Cardiac Arrest. Pediatr Neurol 2020; 108:13-22. [PMID: 32381279 PMCID: PMC7354677 DOI: 10.1016/j.pediatrneurol.2020.03.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 03/03/2020] [Accepted: 03/04/2020] [Indexed: 01/08/2023]
Abstract
Early after pediatric cardiac arrest, families and care providers struggle with the uncertainty of long-term neurological prognosis. Cardiac arrest characteristics such as location, intra-arrest factors, and postarrest events have been associated with outcome. We paid particular attention to postarrest modalities that have been shown to predict neurological outcome. These modalities include neurological examination, somatosensory evoked potentials, electroencephalography, and neuroimaging. There is no one modality that accurately predicts neurological prognosis. Thus, a multimodal approach should be undertaken by both neurologists and intensivists to present a clear and consistent message to families. Methods used for the prediction of long-term neurological prognosis need to be specific enough to identify indivuals with a poor outcome. We review the evidence evaluating children with coma, each with various etiologies of cardiac arrest, outcome measures, and timing of follow-up.
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Affiliation(s)
- Alyssa E Smith
- Division of Pediatric Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri.
| | - Stuart H Friess
- Division of Critical Care Medicine, Department of Pediatrics, Washington University in St. Louis, St. Louis, Missouri
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