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Prognostic Utility of Computed Tomography Histogram Analysis in Patients With Post–Cardiac Arrest Syndrome: Evaluation Using an Automated Whole-Brain Extraction Algorithm

Objective The aim of the study was to evaluate the prognostic utility of computed tomography (CT) histogram analysis with an automated whole-brain extraction algorithm in patients with post–cardiac arrest syndrome (PCAS). Methods Computed tomography data from consecutive patients between January 2009 and February 2012 were obtained and retrospectively analyzed. All CT images were obtained using a 64-detector-row CT scanner with a slice thickness of 4.0 mm. A brain region was extracted from the whole-brain CT images using our original automated algorithm and used for the subsequent histogram analysis. The obtained histogram statistics (mean brain tissue CT value, kurtosis, and skewness), as well as clinical parameters, were compared between the good and poor outcome groups using the Student t test. In addition, receiver operating characteristic curve analysis was performed for the discrimination between the 2 groups for each parameter. Results One hundred thirty-eight consecutive PCAS patients were enrolled. The patients were classified into good (n = 47) and poor (n = 91) outcome groups. The mean brain tissue CT value was significantly higher in the good outcome group than in the poor outcome group (P < 0.05). Kurtosis, skewness, and age were significantly lower in the good outcome group than in the poor outcome group (P < 0.0001, P < 0.05, and P < 0.05, respectively). The area-under-the-curve values for kurtosis, mean brain tissue CT value, skewness, and age were 0.751, 0.639, 0.623, and 0.626, respectively. A combination of the 4 parameters increased the diagnostic performance (area under the curve = 0.814). Conclusions Histogram analysis of whole-brain CT images with our automated extraction algorithm is useful for assessing the outcome of PCAS patients.