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Abstract:Mathematical model simulations may assist in the selection of mechanical ventilator settings. Previously, simulations have been limited to control ventilator modes, as these models lacked representation of respiratory control. This paper presents integration of a chemoreflex respiratory control model with models describing: ventilation and pulmonary gas exchange; oxygenation and acid-base status of blood; circulation; interstitial fluid and tissue buffering; and metabolism. A sensitivity analysis showed that typical response to changing ventilator settings can be described by base excess (BE), production of CO2 (V̇CO 2 ), and model parameters describing central chemoreceptor behavior. Since BE and V̇CO 2 , can be routinely measured, changes in ventilator support may therefore be used to identify patient-specific chemoreceptor drive, enabling patient-specific predictions of the response to changes in mechanical ventilation.

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Incomplete expiration of tidal volume can lead to dynamic hyperinflation and auto-PEEP. Methods are available for assessing these, but are not appropriate for patients with respiratory muscle activity, as occurs in pressure support. Information may exist in expiratory flow and carbon dioxide measurements, which, when taken together, may help characterize dynamic hyperinflation. This paper postulates such patterns and investigates whether these can be seen systematically in data. Two variables are proposed summarizing the number of incomplete expirations quantified as a lack of return to zero flow in expiration (IncExp), and the end tidal CO variability (varETCO), over 20 breaths. Using these variables, three patterns of ventilation are postulated: (a) few incomplete expirations (IncExp < 2) and small varETCO; (b) a variable number of incomplete expirations (2 ≤ IncExp ≤ 18) and large varETCO; and (c) a large number of incomplete expirations (IncExp > 18) and small varETCO. IncExp and varETCO were calculated from data describing respiratory flow and CO signals in 11 patients mechanically ventilated at 5 levels of pressure support. Data analysis showed that the three patterns presented systematically in the data, with periods of IncExp < 2 or IncExp > 18 having significantly lower variability in end-tidal CO than periods with 2 ≤ IncExp ≤ 18 (p < 0.05). It was also shown that sudden change in IncExp from either IncExp < 2 or IncExp > 18 to 2 ≤ IncExp ≤ 18 results in significant, rapid, change in the variability of end-tidal CO p < 0.05. This study illustrates that systematic patterns of expiratory flow and end-tidal CO are present in patients in supported mechanical ventilation, and that changes between these patterns can be identified. Further studies are required to see if these patterns characterize dynamic hyperinflation. If so, then their combination may provide a useful addition to understanding the patient at the bedside.