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Published on: January 5, 2018
Standardised and automated assessment of head computed tomography reliably predicts poor functional outcome after
Margareta Lang1,2, Martin Kenda3,4, Michael Scheel5
1Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
Insights
Standardised head CT scans reliably predict poor outcomes after cardiac arrest. Automated grey-white-matter ratio (GWR) offers a feasible and accessible neuroprognostication tool for all medical centers.
Area of Science:
- Neurology
- Radiology
- Critical Care Medicine
Background:
- Neuroprognostication after cardiac arrest is crucial for guiding clinical decisions.
- Computed tomography (CT) is a readily available imaging modality.
Purpose of the Study:
- To evaluate the application of standardized and automated head CT assessments for neuroprognostication in cardiac arrest survivors.
- To assess the reliability and feasibility of qualitative and quantitative CT analysis.
Main Methods:
- A prospective, international, multicenter observational study (TTM2 trial).
- Standardized qualitative and quantitative head CT assessments were performed by blinded raters.
- Grey-white-matter ratio (GWR) was calculated manually (GWR-4, GWR-8) and via an automated atlas-based approach.
Main Results:
- 140 unconscious patients were included; 75% had poor outcomes.
- Standardized qualitative assessment and all GWR models showed 100% specificity for predicting poor outcomes.
- Automated GWR achieved high prognostic accuracy (AUC 0.84) comparable to manual GWR, with improved interrater agreement.
Conclusions:
- Standardized CT assessments (qualitative and quantitative) are reliable for predicting poor functional outcomes post-cardiac arrest.
- Automated GWR quantification enhances accessibility of CT-based neuroprognostication across centers.
- These methods aid in clinical decision-making for cardiac arrest patients.
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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