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Gramespacher H, Schmieschek MHT, Warnke C, Adler C, Bittner S, Dronse J, Richter N, Zaeske C, Gietzen C, Schlamann M, Baldus S, Fink GR, Onur OA. Analysis of Cerebral CT Based on Supervised Machine Learning as a Predictor of Outcome After Out-of-Hospital Cardiac Arrest. Neurology 2024; 103:e209583. [PMID: 38857458 DOI: 10.1212/wnl.0000000000209583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND AND OBJECTIVES In light of limited intensive care capacities and a lack of accurate prognostic tools to advise caregivers and family members responsibly, this study aims to determine whether automated cerebral CT (CCT) analysis allows prognostication after out-of-hospital cardiac arrest. METHODS In this monocentric, retrospective cohort study, a supervised machine learning classifier based on an elastic net regularized logistic regression model for gray matter alterations on nonenhanced CCT obtained after cardiac arrest was trained using 10-fold cross-validation and tested on a hold-out sample (random split 75%/25%) for outcome prediction. Following the literature, a favorable outcome was defined as a cerebral performance category of 1-2 and a poor outcome of 3-5. The diagnostic accuracy was compared with established and guideline-recommended prognostic measures within the sample, that is, gray matter-white matter ratio (GWR), neuron-specific enolase (NSE), and neurofilament light chain (NfL) in serum. RESULTS Of 279 adult patients, 132 who underwent CCT within 14 days of cardiac arrest with good imaging quality were identified. Our approach discriminated between favorable and poor outcomes with an area under the curve (AUC) of 0.73 (95% CI 0.59-0.82). Thus, the prognostic power outperformed the GWR (AUC 0.66, 95% CI 0.56-0.76). The biomarkers NfL, measured at days 1 and 2, and NSE, measured at day 2, exceeded the reliability of the imaging markers derived from CT (AUC NfL day 1: 0.87, 95% CI 0.75-0.99; AUC NfL day 2: 0.90, 95% CI 0.79-1.00; AUC NSE day: 2 0.78, 95% CI 0.62-0.94). DISCUSSION Our data show that machine learning-assisted gray matter analysis of CCT images offers prognostic information after out-of-hospital cardiac arrest. Thus, CCT gray matter analysis could become a reliable and time-independent addition to the standard workup with serum biomarkers sampled at predefined time points. Prospective studies are warranted to replicate these findings.
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Affiliation(s)
- Hannes Gramespacher
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Maximilian H T Schmieschek
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Clemens Warnke
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Christoph Adler
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Stefan Bittner
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Julian Dronse
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Nils Richter
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Charlotte Zaeske
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Carsten Gietzen
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Marc Schlamann
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Stephan Baldus
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Gereon R Fink
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
| | - Oezguer A Onur
- From the Department of Neurology (H.G., M.H.T.S., C.W., J.D., N.R., G.R.F., O.A.O.), Faculty of Medicine and University Hospital Cologne; Division of Cardiology, Pneumology, Angiology and Intensive Care (C.A., S. Baldus), Department of Internal Medicine III, University of Cologne; Department of Neurology (S. Bittner), University Medical Center Mainz; Cognitive Neuroscience (N.R., O.A.O.), Institute of Neuroscience and Medicine (INM-3), Research Center Jülich; and Institute for Diagnostic and Interventional Radiology (C.Z., C.G., M.S.), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany
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Kenda M, Lang M, Nee J, Hinrichs C, Dell'Orco A, Salih F, Kemmling A, Nielsen N, Wise M, Thomas M, Düring J, McGuigan P, Cronberg T, Scheel M, Moseby-Knappe M, Leithner C. Regional Brain Net Water Uptake in Computed Tomography after Cardiac Arrest - A Novel Biomarker for Neuroprognostication. Resuscitation 2024; 200:110243. [PMID: 38796092 DOI: 10.1016/j.resuscitation.2024.110243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 05/10/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND Selective water uptake by neurons and glial cells and subsequent brain tissue oedema are key pathophysiological processes of hypoxic-ischemic encephalopathy (HIE) after cardiac arrest (CA). Although brain computed tomography (CT) is widely used to assess the severity of HIE, changes of brain radiodensity over time have not been investigated. These could be used to quantify regional brain net water uptake (NWU), a potential prognostic biomarker. METHODS We conducted an observational prognostic accuracy study including a derivation (single center cardiac arrest registry) and a validation (international multicenter TTM2 trial) cohort. Early (<6 h) and follow-up (>24 h) head CTs of CA patients were used to determine regional NWU for grey and white matter regions after co-registration with a brain atlas. Neurological outcome was dichotomized as good versus poor using the Cerebral Performance Category Scale (CPC) in the derivation cohort and Modified Rankin Scale (mRS) in the validation cohort. RESULTS We included 115 patients (81 derivation, 34 validation) with out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA). Regional brain water content remained unchanged in patients with good outcome. In patients with poor neurological outcome, we found considerable regional water uptake with the strongest effect in the basal ganglia. NWU >8% in the putamen and caudate nucleus predicted poor outcome with 100% specificity (95%-CI: 86-100%) and 43% (moderate) sensitivity (95%-CI: 31-56%). CONCLUSION This pilot study indicates that NWU derived from serial head CTs is a promising novel biomarker for outcome prediction after CA. NWU >8% in basal ganglia grey matter regions predicted poor outcome while absence of NWU indicated good outcome. NWU and follow-up CTs should be investigated in larger, prospective trials with standardized CT acquisition protocols.
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Affiliation(s)
- Martin Kenda
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Junior Digital Clinician Scientist Program, Charitéplatz 1, 10117 Berlin, Germany.
| | - Margareta Lang
- Department of Clinical Sciences Lund, Radiology, Lund University, Helsingborg Hospital, Lund, Sweden
| | - Jens Nee
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Nephrology and Intensive Care Medicine, Circulatory Arrest Center Berlin, Berlin, Germany
| | - Carl Hinrichs
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Nephrology and Intensive Care Medicine, Circulatory Arrest Center Berlin, Berlin, Germany
| | - Andrea Dell'Orco
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Neuroradiology, Campus Charité, Mitte, Germany
| | - Farid Salih
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany
| | - André Kemmling
- Department of Neuroradiology, University Hospital Marburg, Marburg, Germany
| | - Niklas Nielsen
- Anaesthesiology and Intensive Care, Department of Clinical Sciences Lund, Helsingborg Hospital, Lund University, Lund, Sweden
| | - Matt Wise
- Adult Critical Care, University Hospital of Wales, Cardiff, UK
| | | | - Joachim Düring
- Department of Clinical Sciences, Anesthesia and Intensive Care, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Peter McGuigan
- Regional Intensive Care Unit, Royal Victoria Hospital, Belfast, UK; Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, UK
| | - Tobias Cronberg
- Department of Neurology, Skane University Hospital, Lund, Sweden
| | - Michael Scheel
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Neuroradiology, Campus Charité, Mitte, Germany
| | - Marion Moseby-Knappe
- Department of Clinical Sciences Lund, Neurology and Rehabilitation, Lund University, Skåne University Hospital, Lund, Sweden
| | - Christoph Leithner
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany
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3
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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 2024:10.1007/s00134-024-07497-2. [PMID: 38900283 DOI: 10.1007/s00134-024-07497-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [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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Bang HJ, Youn CS, Sandroni C, Park KN, Lee BK, Oh SH, Cho IS, Choi SP. Good outcome prediction after out-of-hospital cardiac arrest: A prospective multicenter observational study in Korea (the KORHN-PRO registry). Resuscitation 2024; 199:110207. [PMID: 38582440 DOI: 10.1016/j.resuscitation.2024.110207] [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: 02/07/2024] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
AIM To assess the ability of clinical examination, biomarkers, electrophysiology and brain imaging, individually or in combination to predict good neurological outcomes at 6 months after CA. METHODS This was a retrospective analysis of the Korean Hypothermia Network Prospective Registry 1.0, which included adult out-of-hospital cardiac arrest (OHCA) patients (≥18 years). Good outcome predictors were defined as both pupillary light reflex (PLR) and corneal reflex (CR) at admission, Glasgow Coma Scale Motor score (GCS-M) >3 at admission, neuron-specific enolase (NSE) <17 µg/L at 24-72 h, a median nerve somatosensory evoked potential (SSEP) N20/P25 amplitude >4 µV, continuous background without discharges on electroencephalogram (EEG), and absence of anoxic injury on brain CT and diffusion-weighted imaging (DWI). RESULTS A total of 1327 subjects were included in the final analysis, and their median age was 59 years; among them, 412 subjects had a good neurological outcome at 6 months. GCS-M >3 at admission had the highest specificity of 96.7% (95% CI 95.3-97.8), and normal brain DWI had the highest sensitivity of 96.3% (95% CI 92.9-98.4). When the two predictors were combined, the sensitivities tended to decrease (ranging from 2.7-81.1%), and the specificities tended to increase, ranging from81.3-100%. Through the explorative variation of the 2021 European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) prognostication strategy algorithms, good outcomes were predicted, with a specificity of 83.2% and a sensitivity of 83.5% in patients by the algorithm. CONCLUSIONS Clinical examination, biomarker, electrophysiology, and brain imaging predicted good outcomes at 6 months after CA. When the two predictors were combined, the specificity further improved. With the 2021 ERC/ESICM guidelines, the number of indeterminate patients and the uncertainty of prognostication can be reduced by using a good outcome prediction algorithm.
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Affiliation(s)
- Hyo Jin Bang
- Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Chun Song Youn
- Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea.
| | - Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"-IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy
| | - Kyu Nam Park
- Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Byung Kook Lee
- Department of Emergency Medicine, Chonnam National University Hospital, 42, Jebong-ro, Donggu, Gwangju, Republic of Korea
| | - Sang Hoon Oh
- Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - In Soo Cho
- Department of Emergency Medicine, KEPCO Medical Center, 308, Uicheon-ro, Dobong-gu, Seoul, Republic of Korea
| | - Seung Pill Choi
- Department of Emergency Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea
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Yoon JA, Kang C, Park JS, You Y, Min JH, In YN, Jeong W, Ahn HJ, Jeong HS, Kim YH, Lee BK, Kim D. Quantitative analysis of apparent diffusion coefficients to predict neurological prognosis in cardiac arrest survivors: an observational derivation and internal-external validation study. Crit Care 2024; 28:138. [PMID: 38664807 PMCID: PMC11044301 DOI: 10.1186/s13054-024-04909-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 04/11/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND This study aimed to validate apparent diffusion coefficient (ADC) values and thresholds to predict poor neurological outcomes in out-of-hospital cardiac arrest (OHCA) survivors by quantitatively analysing the ADC values via brain magnetic resonance imaging (MRI). METHODS This observational study used prospectively collected data from two tertiary academic hospitals. The derivation cohort comprised 70% of the patients randomly selected from one hospital, whereas the internal validation cohort comprised the remaining 30%. The external validation cohort used the data from another hospital, and the MRI data were restricted to scans conducted at 3 T within 72-96 h after an OHCA experience. We analysed the percentage of brain volume below a specific ADC value at 50-step intervals ranging from 200 to 1200 × 10-6 mm2/s, identifying thresholds that differentiate between good and poor outcomes. Poor neurological outcomes were defined as cerebral performance categories 3-5, 6 months after experiencing an OHCA. RESULTS A total of 448 brain MRI scans were evaluated, including a derivation cohort (n = 224) and internal/external validation cohorts (n = 96/128, respectively). The proportion of brain volume with ADC values below 450, 500, 550, 600, and 650 × 10-6 mm2/s demonstrated good to excellent performance in predicting poor neurological outcomes in the derivation group (area under the curve [AUC] 0.89-0.91), and there were no statistically significant differences in performances among the derivation, internal validation, and external validation groups (all P > 0.5). Among these, the proportion of brain volume with an ADC below 600 × 10-6 mm2/s predicted a poor outcome with a 0% false-positive rate (FPR) and 76% (95% confidence interval [CI] 68-83) sensitivity at a threshold of > 13.2% in the derivation cohort. In both the internal and external validation cohorts, when using the same threshold, a specificity of 100% corresponded to sensitivities of 71% (95% CI 58-81) and 78% (95% CI 66-87), respectively. CONCLUSIONS In this validation study, by consistently restricting the MRI types and timing during quantitative analysis of ADC values in brain MRI, we observed high reproducibility and sensitivity at a 0% FPR. Prospective multicentre studies are necessary to validate these findings.
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Affiliation(s)
- Jung A Yoon
- 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
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - Jung Soo Park
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea.
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea.
| | - Yeonho You
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - Jin Hong Min
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
- Department of Emergency Medicine, Chungnam National University Sejong Hospital, Daejoen, Republic of Korea
| | - Yong Nam In
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
- Department of Emergency Medicine, Chungnam National University Sejong Hospital, Daejoen, Republic of Korea
| | - Wonjoon Jeong
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - Hong Jun Ahn
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - Hye Seon Jeong
- Department of Neurology, Chungnam National University Hospital, Daejoen, Republic of Korea
| | - Yong Hwan Kim
- Department of Emergency Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Gyeongsangnam-do, Republic of Korea
| | - Byung Kook Lee
- Department of Emergency Medicine, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Dongha Kim
- Department of Statistics, Sungshin Women's University, Seoul, Republic of Korea
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Tsai H, Chi CY, Wang LW, Su YJ, Chen YF, Tsai MS, Wang CH, Hsu C, Huang CH, Wang W. Outcome prediction of cardiac arrest with automatically computed gray-white matter ratio on computed tomography images. Crit Care 2024; 28:118. [PMID: 38594772 PMCID: PMC11005205 DOI: 10.1186/s13054-024-04895-2] [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: 12/22/2023] [Accepted: 03/29/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND This study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes. METHODS Patients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge. We proposed an automated method comprising image registration, K-means segmentation, segmentation refinement, and GWR calculation to measure the GWR for each CT scan. The K-means segmentation and segmentation refinement was employed to refine the segmentations within regions of interest (ROIs), consequently enhancing GWR calculation accuracy through more precise segmentations. RESULTS Overall, 443 patients were divided into derivation N=265, 60% and validation N=178, 40% sets, based on age and sex. The ROI Hounsfield unit values derived from the automated method showed a strong correlation with those obtained from the manual method. Regarding outcome prediction, the automated method significantly outperformed the manual method in GWR calculation (AUC 0.79 vs. 0.70) across the entire dataset. The automated method also demonstrated superior performance across sensitivity, specificity, and positive and negative predictive values using the cutoff value determined from the derivation set. Moreover, GWR was an independent predictor of outcomes in logistic regression analysis. Incorporating the GWR with other clinical and resuscitation variables significantly enhanced the performance of prediction models compared to those without the GWR. CONCLUSIONS Automated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.
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Affiliation(s)
- Hsinhan Tsai
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 106216, Taiwan R.O.C
| | - Chien-Yu Chi
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Liang-Wei Wang
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Yu-Jen Su
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Ya-Fang Chen
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Min-Shan Tsai
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Chih-Hung Wang
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Cheyu Hsu
- Department of Oncology, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Chien-Hua Huang
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C..
| | - Weichung Wang
- Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, 106216, Taiwan R.O.C..
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Mathur R, Meyfroidt G, Robba C, Stevens RD. Neuromonitoring in the ICU - what, how and why? Curr Opin Crit Care 2024; 30:99-105. [PMID: 38441121 DOI: 10.1097/mcc.0000000000001138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
PURPOSE OF REVIEW We selectively review emerging noninvasive neuromonitoring techniques and the evidence that supports their use in the ICU setting. The focus is on neuromonitoring research in patients with acute brain injury. RECENT FINDINGS Noninvasive intracranial pressure evaluation with optic nerve sheath diameter measurements, transcranial Doppler waveform analysis, or skull mechanical extensometer waveform recordings have potential safety and resource-intensity advantages when compared to standard invasive monitors, however each of these techniques has limitations. Quantitative electroencephalography can be applied for detection of cerebral ischemia and states of covert consciousness. Near-infrared spectroscopy may be leveraged for cerebral oxygenation and autoregulation computation. Automated quantitative pupillometry and heart rate variability analysis have been shown to have diagnostic and/or prognostic significance in selected subtypes of acute brain injury. Finally, artificial intelligence is likely to transform interpretation and deployment of neuromonitoring paradigms individually and when integrated in multimodal paradigms. SUMMARY The ability to detect brain dysfunction and injury in critically ill patients is being enriched thanks to remarkable advances in neuromonitoring data acquisition and analysis. Studies are needed to validate the accuracy and reliability of these new approaches, and their feasibility and implementation within existing intensive care workflows.
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Affiliation(s)
- Rohan Mathur
- Department of Anesthesiology & Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Geert Meyfroidt
- Department of Intensive Care Medicine, University Hospitals Leuven, Belgium and Laboratory of Intensive Care Medicine, Department of Cellular and Molecular Medicine, KU Leuven, Belgium
| | - Chiara Robba
- IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Scienze Chirurgiche e Diagnostiche Integrate, Università degli Studi di Genova, Genova, Italy
| | - Robert D Stevens
- Department of Anesthesiology & Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, USA
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Molinski NS, Kenda M, Leithner C, Nee J, Storm C, Scheel M, Meddeb A. Deep learning-enabled detection of hypoxic-ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches. Front Neurosci 2024; 18:1245791. [PMID: 38419661 PMCID: PMC10899383 DOI: 10.3389/fnins.2024.1245791] [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: 06/23/2023] [Accepted: 01/31/2024] [Indexed: 03/02/2024] Open
Abstract
Objective To establish a deep learning model for the detection of hypoxic-ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format. Methods 168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.6%) without signs of HIE. These images were randomly divided into a training and a test set, and five deep learning models based on based on Densely Connected Convolutional Networks (DenseNet121) were trained and validated using different image input formats (2D and 3D images). Results All optimized stacked 2D and 3D networks could detect signs of HIE. The networks based on the data as 2D image data stacks provided the best results (S100: AUC: 94%, ACC: 79%, S50: AUC: 93%, ACC: 79%). We provide visual explainability data for the decision making of our AI model using Gradient-weighted Class Activation Mapping. Conclusion Our proof-of-concept deep learning model can accurately identify signs of HIE on CT images. Comparing different 2D- and 3D-based approaches, most promising results were achieved by 2D image stack models. After further clinical validation, a deep learning model of HIE detection based on CT images could be implemented in clinical routine and thus aid clinicians in characterizing imaging data and predicting outcome.
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Affiliation(s)
- Noah S. Molinski
- Department for Neuroradiology, Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Martin Kenda
- Department of Neurology with Experimental Neurology, Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Berlin, Germany
| | - Christoph Leithner
- Department of Neurology with Experimental Neurology, Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jens Nee
- Department of Nephrology and Medical Intensive Care, Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Christian Storm
- Department of Nephrology and Medical Intensive Care, Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Michael Scheel
- Department for Neuroradiology, Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Aymen Meddeb
- Department for Neuroradiology, Charité – Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Berlin, Germany
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9
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Case NP, Callaway CW, Elmer J, Coppler PJ. Simple approach to quantify hypoxic-ischemic brain injury severity from computed tomography imaging files after cardiac arrest. Resuscitation 2024; 195:110050. [PMID: 37977348 PMCID: PMC10922650 DOI: 10.1016/j.resuscitation.2023.110050] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Grey-white ratio (GWR) can estimate severity of cytotoxic cerebral edema secondary to hypoxic-ischemic brain injury after cardiac arrest and predict progression to death by neurologic criteria (DNC). Current approaches to calculating GWR are not standardized and have variable interrater reliability. We tested if measures of variance of brain density on early computed tomographic (CT) imaging after cardiac arrest could predict DNC. METHODS We performed a retrospective cohort study, identifying post-arrest patients treated between 2011 and 2020 at our single center. We extracted demographic data from our registry and Digital Imaging and Communication in Medicine (DICOM) files for each patient's first brain CT. We analyzed slices 15-20 of each DICOM, corresponding to the level of the basal ganglia while accommodating differences in patient anatomy. We extracted pixel arrays and converted the radiodensities to Hounsfield units (HU). To focus on brain tissue densities, we excluded HU > 60 and < 10. We calculated the variance of each patient's HU distribution and the difference between the means of a two-group Gaussian finite mixture model. We compared these novel metrics to existing measures of cerebral edema, then randomly divided our data into 80% training and 20% test sets and used logistic regression to predict DNC. RESULTS Of 1,133 included subjects, 457 (40%) were female, mean (standard deviation) age was 58 (16) years, and 115 (10%) progressed to DNC. CTs were obtained a median [interquartile range] of 4.2 [2.8-5.7] hours post-arrest. Our novel measures correlated weakly with GWR. HU variance, but not difference between mixture model means, differed significantly between subjects with and without sulcal or cistern effacement. GWR outperformed our novel measures in predicting progression to DNC with an area under the receiver operating characteristic curve (AUC) of 0.82, compared to HU variance (AUC = 0.73) and the difference between mixture model means (AUC = 0.56). CONCLUSION There are differences in the distribution of HU on post-arrest CT in patients with qualitative measures of cerebral edema. Current methods to quantify cerebral edema outperform simple measures of attenuation variance on early brain CT. Further analyses could investigate if these measures of variance, or other distributional characteristics of brain density, have improved predictive performance on brain CTs obtained later in the clinical course or derived from discrete regions of anatomical interest.
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Affiliation(s)
- Nicholas P Case
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Clifton W Callaway
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jonathan Elmer
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Patrick J Coppler
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
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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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11
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Czimmeck C, Kenda M, Aalberts N, Endisch C, Ploner CJ, Storm C, Nee J, Streitberger KJ, Leithner C. Confounders for prognostic accuracy of neuron-specific enolase after cardiac arrest: A retrospective cohort study. Resuscitation 2023; 192:109964. [PMID: 37683997 DOI: 10.1016/j.resuscitation.2023.109964] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/10/2023]
Abstract
AIM To evaluate neuron-specific enolase (NSE) thresholds for prediction of neurological outcome after cardiac arrest and to analyze the influence of hemolysis and confounders. METHODS Retrospective analysis from a cardiac arrest registry. Determination of NSE serum concentration and hemolysis-index (h-index) 48-96 hours after cardiac arrest. Evaluation of neurological outcome using the Cerebral Performance Category score (CPC) at hospital discharge. Separate analyses considering CPC 1-3 and CPC 1-2 as good neurological outcome. Analysis of specificity and sensitivity for poor and good neurological outcome prediction with and without exclusion of hemolytic samples (h-index larger than 50). RESULTS Among 356 survivors three days after cardiac arrest, hemolysis was detected in 28 samples (7.9%). At a threshold of 60 µg/L, NSE predicted poor neurological outcome (CPC 4-5) in all samples with a specificity of 92% (86-95%) and sensitivity of 73% (66-79%). In non-hemolytic samples, specificity was 94% (89-97%) and sensitivity 70% (62-76%). At a threshold of 100 µg/L, specificity was 98% (95-100%, all samples) and 99% (95-100%, non-hemolytic samples), and sensitivity 58% (51-65%) and 55% (47-63%), respectively. Possible confounders for elevated NSE in patients with good neurological outcome were ECMO, malignancies, blood transfusions and acute brain diseases. Nine patients with NSE below 17 µg/L had CPC 5, all had plausible death causes other than hypoxic-ischemic encephalopathy. CONCLUSIONS NSE concentrations higher than 100 µg/L predicted poor neurological outcome with high specificity. An NSE less than 17 µg/L indicated absence of severe hypoxic-ischemic encephalopathy. Hemolysis and other confounders need to be considered. INSTITUTIONAL PROTOCOL NUMBER The local ethics committee (board name: Ethikkommission der Charité) approved this study by the number: EA2/066/23, approval date: 28th June 2023, study title "'ROSC' - Resuscitation Outcome Study."
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Affiliation(s)
- Constanze Czimmeck
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany.
| | - Martin Kenda
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Junior Digital Clinician Scientist Program, Charitéplatz 1, 10117 Berlin, Germany
| | - Noelle Aalberts
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Christian Endisch
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Christoph J Ploner
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Christian Storm
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Nephrology and Medical Intensive Care, Charitéplatz 1, 10117 Berlin, Germany
| | - Jens Nee
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Nephrology and Medical Intensive Care, Circulatory Arrest Center of Excellence Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Kaspar J Streitberger
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Christoph Leithner
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany
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Yoon JA, Kang C, Park JS, You Y, Min JH, In YN, Jeong W, Ahn HJ, Lee IH, Jeong HS, Lee BK, Lee JK. Quantitative analysis of early apparent diffusion coefficient values from MRIs for predicting neurological prognosis in survivors of out-of-hospital cardiac arrest: an observational study. Crit Care 2023; 27:407. [PMID: 37880777 PMCID: PMC10599006 DOI: 10.1186/s13054-023-04696-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/19/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND This study aimed to quantitatively analyse ultra-early brain diffusion-weighted magnetic resonance imaging (DW-MRI) findings to determine the apparent diffusion coefficient (ADC) threshold associated with neurological outcomes in comatose survivors of out-of-hospital cardiac arrest (OHCA). METHODS This retrospective study included adult survivors of comatose OHCA who underwent DW-MRI imaging scans using a 3-T MRI scanner within 6 h of the return of spontaneous circulation (ROSC). We investigated the association between neurological outcomes and ADC values obtained through voxel-based analysis on DW-MRI. Additionally, we constructed multivariable logistic regression models with pupillary light reflex (PLR), serum neuron-specific enolase (NSE), and ADC values as independent variables to predict poor neurological outcomes. The primary outcome was poor neurological outcome 6 months after ROSC, determined by the Cerebral Performance Category 3-5. RESULTS Overall, 131 patients (26% female) were analysed, of whom 74 (57%) showed poor neurological outcomes. The group with a poor neurological outcome had lower mean whole brain ADC values (739.1 vs. 787.1 × 10-6 mm/s) and higher percentages of voxels with ADC below threshold in all ranges (250-1150) (all P < 0.001). The mean whole brain ADC values (area under the receiver operating characteristic curve [AUC] 0.83) and the percentage of voxels with ADC below 600 (AUC 0.81) had the highest sensitivity of 51% (95% confidence interval [CI] 39.4-63.1; cut-off value ≤ 739.2 × 10-6 mm2/s and > 17.2%, respectively) when the false positive rate (FPR) was 0%. In the multivariable model, which also included PLR, NSE, and mean whole brain ADC values, poor neurological outcome was predicted with the highest accuracy (AUC 0.91; 51% sensitivity). This model showed more accurate prediction and sensitivity at an FPR of 0% than did the combination of PLR and NSE (AUC 0.86; 30% sensitivity; P = 0.03). CONCLUSIONS In this cohort study, early voxel-based quantitative ADC analysis after ROSC was associated with poor neurological outcomes 6 months after cardiac arrest. The mean whole brain ADC value demonstrated the highest sensitivity when the FPR was 0%, and including it in the multivariable model improved the prediction of poor neurological outcomes.
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Affiliation(s)
- Jung A Yoon
- 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
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - Jung Soo Park
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea.
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea.
| | - Yeonho You
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - Jin Hong Min
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
- Department of Emergency Medicine, Sejong Chungnam National University Hospital, Daejoen, Republic of Korea
| | - Yong Nam In
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, 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
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - Hong Joon Ahn
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - In Ho Lee
- Department of Radiology, College of Medicine, Chungnam National University, 266, Munhwa-ro, Jung-gu, Daejeon, Republic of Korea
| | - Hye Seon Jeong
- Department of Neurology, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Byung Kook Lee
- Department of Emergency Medicine, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Jae Kwang Lee
- Department of Emergency Medicine, College of Medicine, Konyang University Hospital, Daejeon, Republic of Korea
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Kenda M, Leithner C. On the path to artificial intelligence analysis of brain CT after cardiac arrest. Resuscitation 2023; 191:109947. [PMID: 37634861 DOI: 10.1016/j.resuscitation.2023.109947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 08/11/2023] [Indexed: 08/29/2023]
Affiliation(s)
- Martin Kenda
- Charité - Universitätsmedizin Berlin, Corporate member of Freie Universität and Humboldt-Universität zu Berlin, Department of Neurology, Augustenburger Platz 1, 13353 Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Junior Digital Clinician Scientist Program, Charitéplatz 1, 10117 Berlin, Germany
| | - Christoph Leithner
- Charité - Universitätsmedizin Berlin, Corporate member of Freie Universität and Humboldt-Universität zu Berlin, Department of Neurology, Augustenburger Platz 1, 13353 Berlin, Germany.
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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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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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