liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Standardised and automated assessment of head computed tomography reliably predicts poor functional outcome after cardiac arrest: a prospective multicentre study
Lund Univ, Sweden; Helsingborg Hosp, Sweden.
Charite Univ Med Berlin, Germany; Freie Univ, Germany; Humboldt Univ, Germany.
Charite Univ Med Berlin, Germany.
Univ Helsinki, Finland.
Show others and affiliations
2024 (English)In: Intensive Care Medicine, ISSN 0342-4642, E-ISSN 1432-1238, Vol. 50, no 7, p. 1096-1107Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
SPRINGER , 2024. Vol. 50, no 7, p. 1096-1107
Keywords [en]
Cardiac arrest; Computed tomography; Prognosis; Hypoxic-ischaemic encephalopathy; GWR
National Category
Anesthesiology and Intensive Care
Identifiers
URN: urn:nbn:se:liu:diva-206650DOI: 10.1007/s00134-024-07497-2ISI: 001251781400001PubMedID: 38900283Scopus ID: 2-s2.0-85196384410OAI: oai:DiVA.org:liu-206650DiVA, id: diva2:1891347
Note

Funding Agencies|Vetenskapsrdet

Available from: 2024-08-22 Created: 2024-08-22 Last updated: 2025-06-27

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Search in DiVA

By author/editor
Halliday, Thomas
By organisation
ANOPIVA US
In the same journal
Intensive Care Medicine
Anesthesiology and Intensive Care

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 35 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf