Diagnostic Accuracy of an Algorithm for Discriminating Presumed Solid and Gaseous Microembolic Signals During Transcranial Doppler Examinations

Objective: The aim of the work described here was to assess the diagnostic accuracy of a new algorithm (SGA-a) for time-domain analysis of transcranial Doppler audio signals to discriminate presumed solid and gaseous microem-bolic signals and artifacts (SGAs).Methods: SGA-a was validated by human experts in an artifact cohort of 20 patients subjected to a 1-h transcranial Doppler exam before cardiac surgery (cohort 1). Emboli were validated in a cohort of 10 patients after aortic valve replacement in a 4-h monitoring period (cohort 2). The SGA misclassification rate was estimated by testing SGA-a on artifact-free test files of solid and gaseous emboli. Results: In cohort 1 (n = 24,429), artifacts were classified with an accuracy of 94.5%. In cohort 2 (n = 12,328), the accuracy in discriminating solid/gaseous emboli from artifacts was 85.6%. The 95% limits of agreement for, respectively, the numbers of presumed solids and gaseous emboli, artifacts and microembolic signals of undeter-mined origin were [-10, 10], [-14, 7] and [-9, 16], and the intra-class correction coefficients were 0.99, 0.99 and 0.99, respectively. The rate of misclassification of solid test files was 2%, and the rate of misclassification of gaseous test files was 12%. Conclusion: SGA-a can detect presumed solid and gaseous microembolic signals and differentiate them from arti-facts. SGA-a could be of value when both solid and gaseous emboli may jeopardize brain function such as seen during cardiac valve and/or aortic arch replacement procedures.