Details

Autor(en) / Beteiligte

• Kenda, Martin
• Scheel, Michael
• Kemmling, André
• Aalberts, Noelle
• Guettler, Christopher
• Streitberger, Kaspar J
• Storm, Christian
• Ploner, Christoph J
• Leithner, Christoph

Titel

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

Ist Teil von

• Critical care medicine, 2021-12, Vol.49 (12), p.e1212-e1222

Ort / Verlag

United States

Erscheinungsjahr

2021

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. 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. ICUs at a large, academic hospital with circulatory arrest center. We identified 433 cardiac arrest patients from a large previously established database with brain CTs within 10 days after cardiac arrest. None. 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. 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.