Automated Assessment of Brain CT After Cardiac Arrest—An Observational Derivation/Validation Cohort Study. Issue 12 (December 2021)

• Record Type: Journal Article
• Authors: Kenda, Martin; Scheel, Michael; Kemmling, André; Aalberts, Noelle; Guettler, Christopher; Streitberger, Kaspar J.; Storm, Christian; Ploner, Christoph J.; Leithner, Christoph
• Is Part Of: Critical care medicine. Volume 49:Issue 12(2021)
• Publication Date: 2021-12
• Subjects: automated image analysis -- brain computed tomography -- cardiac arrest -- gray-white-matter ratio; Critical care medicine -- Periodicals; Soins intensifs -- Périodiques; 616.028
• DOI: 10.1097/CCM.0000000000005198
• Languages: English
• ISSNs: 0090-3493

Abstract

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. Abstract : Supplemental Digital Content is available in the text.