In vitro performance evaluation of AnaConDaTM-100 and AnaConDaTM-50 compared to a circle breathing system for control and consumption of volatile anaesthetics

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Abstract

To identify the better volatile anaesthetic delivery system in an intensive care setting, we compared the circle breathing system and two models of reflection systems (AnaConDa™ with a dead space of 100 ml (ACD-100) or 50 ml (ACD-50)). These systems were analysed for the parameters like wash-in, consumption, and wash-out of isoflurane and sevoflurane utilising a test lung model. The test lung was connected to a respirator (circle breathing system: Aisys CS™; ACD-100/50: Puriton Bennett 840). Set parameters were volume-controlled mode, tidal volume-500 ml, respiratory rate-10/min, inspiration time-2 sec, PEEP-5 mbar, and oxygen-21%. Wash-in, consumption, and wash-out were investigated at fresh gas flows of 0.5, 1.0, 2.5, and 5.0 l/min. Anaesthetic target concentrations were 0.5, 1.0, 1.5, 2.0, and 2.5%. Wash-in was slower in ACD-100/-50 compared to the circle breathing system, except for fresh gas flows of 0.5 and 1.0 l/min. The consumption of isoflurane and sevoflurane in ACD-100 and ACD-50 corresponded to the fresh gas flow of 0.5-1.0 l/min in the circle breathing system. Consumption with ACD-50 was higher in comparison to ACD-100, especially at gas concentrations > 1.5%. Wash-out was quicker in ACD-100/-50 than in the circle breathing system at a fresh gas flow of 0.5 l/min, however, it was longer at all the other flow rates. Wash-out was comparable in ACD-100 and ACD-50. Wash-in and wash-out were generally quicker with the circle breathing system than in ACD-100/-50. However, consumption at 0.5 minimum alveolar concentration was comparable at flows of 0.5 and 1.0 l/min.

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Bellgardt, M., Drees, D., Vinnikov, V., Georgevici, A. I., Procopiuc, L., Weber, T. P., … Herzog-Niescery, J. (2022). In vitro performance evaluation of AnaConDaTM-100 and AnaConDaTM-50 compared to a circle breathing system for control and consumption of volatile anaesthetics. Journal of Clinical Monitoring and Computing, 36(1), 169–177. https://doi.org/10.1007/s10877-020-00634-4

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