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Suitable CO2 Solubility Models for Determination of the CO2 Removal Performance of Oxygenators.

Benjamin LukitschPaul EckerMartin ElenkovChristoph JaneczekChristian JordanClaus G KrennRoman UllrichMargit GföhlerMichael Harasek
Published in: Bioengineering (Basel, Switzerland) (2021)
CO2 removal via membrane oxygenators during lung protective ventilation has become a reliable clinical technique. For further optimization of oxygenators, accurate prediction of the CO2 removal rate is necessary. It can either be determined by measuring the CO2 content in the exhaust gas of the oxygenator (sweep flow-based) or using blood gas analyzer data and a CO2 solubility model (blood-based). In this study, we determined the CO2 removal rate of a prototype oxygenator utilizing both methods in in vitro trials with bovine and in vivo trials with porcine blood. While the sweep flow-based method is reliably accurate, the blood-based method depends on the accuracy of the solubility model. In this work, we quantified performances of four different solubility models by calculating the deviation of the CO2 removal rates determined by both methods. Obtained data suggest that the simplest model (Loeppky) performs better than the more complex ones (May, Siggaard-Anderson, and Zierenberg). The models of May, Siggaard-Anderson, and Zierenberg show a significantly better performance for in vitro bovine blood data than for in vivo porcine blood data. Furthermore, the suitability of the Loeppky model parameters for bovine blood (in vitro) and porcine blood (in vivo) is evaluated.
Keyphrases
  • electronic health record
  • big data
  • high resolution
  • machine learning
  • artificial intelligence
  • acute respiratory distress syndrome