• J Clin Monit Comput · Feb 2014

    Case Reports

    USCOM-window to the circulation: utility of supra-sternal Doppler in an elderly anaesthetized patient for a robotic cystectomy.

    • Lester A H Critchley and Li Huang.
    • Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, China, hcritchley@cuhk.edu.hk.
    • J Clin Monit Comput. 2014 Feb 1;28(1):83-93.

    AbstractSupra-sternal Doppler (USCOM Ltd., Sydney, Australia) can be used during anaesthesia to measure cardiac output (CO) and related flow parameters. However, before the USCOM can be used routinely, its utility and limitations need to be fully understood and critical information about its use disseminated. In "Window to the Circulation" we use the example of an elderly man undergoing major urological robotic surgery to highlight the utility and limitations of intra-operative USCOM use. USCOM readings were verified against oesophageal Doppler. Despite the lack of major blood loss (<500 ml in 8-h), significant changes in haemodynamics were recorded. CO ranged from 3.2 to 8.3 l/min. The quality of USCOM scans and reliability of data was initially poor, but improved as CO increased as surgery progressed. When USCOM scans became acceptable the correlation with oesophageal Doppler was R(2) = 8.0 (p < 0.001). Several characteristic features of the supra-sternal Doppler scans were identified: Aortic and pulmonary flow waves, valve closure, E and A waves, false A-wave and aberrant arterial flow patterns. Their identification helped with identifying the main flow signal across the aortic valve. The USCOM has the potential to monitor changes in CO and related flow parameters intra-operatively and thus help the anaesthetist to more fully understand the patient's haemodynamics. However, achieving a good quality scan is important as it improves the reliability of USCOM data. The supra-sternal route is rich in flow signals and identifying the aortic valve signal is paramount. Recognizing the other characteristic waveforms in the signal helps greatly.

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