Anaesthesia and intensive care
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Anaesth Intensive Care · Sep 2022
Successful laryngoscope view using oversized C-Mac® D-blade in children presenting with difficult airway.
Management of the difficult paediatric airway is challenging due to anatomical differences, congenital anomalies, paucity of paediatric airway management algorithms, and the limited availability of paediatric-sized airway devices. In this case report, we describe two cases highlighting the potential use of seemingly oversized videolaryngoscopes in the management of the difficult paediatric airway. Recognising the cause of difficult airway in the paediatric population is potentially useful in the selection of a larger videolaryngoscope blade to aid laryngoscopy and intubation.
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Anaesth Intensive Care · Sep 2022
An audit of the diagnostic accuracy of the ROTEM®sigma for the identification of hypofibrinogenaemia in cardiac surgical patients.
The ROTEM®delta (TEM Innovations GmbH, Munich, Germany) has been used extensively worldwide for the assessment of coagulation in cardiac surgical patients. Recently, a new cartridge-based ROTEM device (ROTEM®sigma, TEM Innovations GmbH, Munich, Germany) has become available. In this paper we report an audit of the diagnostic accuracy of the ROTEM sigma for the identification of hypofibrinogenaemia in cardiac surgical patients. ⋯ The predictive values were also in a similar range to those previously reported for the ROTEMdelta, with low false negative rates (2% for A10 ≤8 mm; 3% for A5 ≤6 mm). These findings support the use of the ROTEMsigma as an alternative to the ROTEMdelta for the identification of hypofibrinogenaemia post-cardiopulmonary bypass in cardiac surgical patients. However, further studies are required in other settings.
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Anaesth Intensive Care · Sep 2022
A prediction model to determine the untapped lung donor pool outside of the DonateLife network in Victoria.
Lung transplantation is limited by a lack of suitable lung donors. In Australia, the national donation organisation (DonateLife) has taken a major role in optimising organ donor identification. However, the potential outside the DonateLife network hospitals remains uncertain. ⋯ Applying the model to non-DonateLife hospital data predicted only an additional five lung donors. This prediction model revealed few additional lung donor opportunities outside the DonateLife network, and the necessity of alternative and novel strategies for lung donation. A donor prediction model could provide a useful benchmarking tool to explore organ donation potential across different jurisdictions, hospitals and transplanting centres.