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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] [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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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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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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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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