Automated Assessment of Brain CT After Cardiac Arrest—An Observational Derivation/Validation Cohort Study

Abstract

OBJECTIVES: Prognostication of outcome is an essential step in defining therapeutic goals after cardiac arrest. Gray-white-matter ratio obtained from brain CT can predict poor outcome. However, manual placement of regions of interest is a potential source of error and interrater variability. Our objective was to assess the performance of poor outcome prediction by automated quantification of changes in brain CTs after cardiac arrest. DESIGN: Observational, derivation/validation cohort study design. Outcome was determined using the Cerebral Performance Category upon hospital discharge. Poor outcome was defined as death or unresponsive wakefulness syndrome/coma. CTs were automatically decomposed using coregistration with a brain atlas. SETTING: ICUs at a large, academic hospital with circulatory arrest center. PATIENTS: We identified 433 cardiac arrest patients from a large previously established database with brain CTs within 10 days after cardiac arrest. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Five hundred sixteen brain CTs were evaluated (derivation cohort n = 309, validation cohort n = 207). Patients with poor outcome had significantly lower radiodensities in gray matter regions. Automated GWR_si (putamen/posterior limb of internal capsule) was performed with an area under the curve of 0.86 (95%-CI: 0.80-0.93) for CTs taken later than 24 hours after cardiac arrest (similar performance in the validation cohort). Poor outcome (Cerebral Performance Category 4–5) was predicted with a specificity of 100% (95% CI, 87–100%, derivation; 88–100%, validation) at a threshold of less than 1.10 and a sensitivity of 49% (95% CI, 36–58%, derivation) and 38% (95% CI, 27–50%, validation) for CTs later than 24 hours after cardiac arrest. Sensitivity and area under the curve were lower for CTs performed within 24 hours after cardiac arrest. CONCLUSIONS: Automated gray-white-matter ratio from brain CT is a promising tool for prediction of poor neurologic outcome after cardiac arrest with high specificity and low-to-moderate sensitivity. Prediction by gray-white-matter ratio at the basal ganglia level performed best. Sensitivity increased considerably for CTs performed later than 24 hours after cardiac arrest.

A pilot study of methods for prediction of poor outcome by head computed tomography after cardiac arrest

“…Automated GWR measurements in CTs performed ! 24 h post-arrest reported AUROC of 0.86, which is in accordance with our results on later CT. 17,32 Nonetheless, these tools are currently not widely available and require further evaluation in prospective trials. Future studies should aim at improving interrater reliability of CT as a prognostic method for prediction of poor outcome after CA.…”

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

“…Crosses indicate the ROIs included in each grey-white matter ratio method: 8 BG (basal ganglia model), 4SI (simple model) and auto GWR (automated GWR determination). 17. …”

“…CT scans will be co-registered to an MRI-based digital standard brain using the free analysis software FSL-FMRIB Software v5.0 by Analysis Group, FMRIB, Oxford, UK) as previously described. 17 Tissue probability maps derived from standard brain atlases will then be used to automatically identify anatomical regions on each CT scan. GWR will be calculated by the densities of the putamen and of the posterior limb of the internal capsule ( Fig.…”

“… Grey-white matter ratio (GWR) at the basal ganglia level below pre-specified cutoffs (1.10 and 1.15) determined using our SOP for quantitative assessment. Automated, atlas-based GWR on the basal ganglia level (auto GWR) below 1.10 as described by Kenda et al 17 . …”

“…Automated, atlas-based GWR on the basal ganglia level (auto GWR) below 1.10 as described by Kenda et al 17 .…”