Typical patterns of expiratory flow and carbon dioxide in mechanically ventilated patients with spontaneous breathing.

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 CO2 variability (varETCO2), over 20 breaths. Using these variables, three patterns of ventilation are postulated: (a) few incomplete expirations (IncExp < 2) and small varETCO2; (b) a variable number of incomplete expirations (2 ≤ IncExp ≤ 18) and large varETCO2; and (c) a large number of incomplete expirations (IncExp > 18) and small varETCO2. IncExp and varETCO2 were calculated from data describing respiratory flow and CO2 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 CO2 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 CO2 p < 0.05. This study illustrates that systematic patterns of expiratory flow and end-tidal CO2 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.

A mathematical model approach quantifying patients' response to changes in mechanical ventilation: Evaluation in pressure support.

PURPOSE: This article evaluates how mathematical models of gas exchange, blood acid-base status, chemical respiratory drive, and muscle function can describe the respiratory response of spontaneously breathing patients to different levels of pressure support. METHODS: The models were evaluated with data from 12 patients ventilated in pressure support ventilation. Models were tuned with clinical data (arterial blood gas measurement, ventilation, and respiratory gas fractions of O2 and CO2) to describe each patient at the clinical level of pressure support. Patients were ventilated up to 5 different pressure support levels, for 15 minutes at each level to achieve steady-state conditions. Model-simulated values of respiratory frequency (fR), arterial pH (pHa), and end-tidal CO2 (FeCO2) were compared to measured values at each pressure support level. RESULTS: Model simulations compared well to measured data with Bland-Altman bias and limits of agreement of fR of 0.7 ± 2.2 per minute, pHa of -0.0007 ± 0.019, and FeCO2 of -0.001 ± 0.003. CONCLUSION: The models describe patients' fR, pHa, and FeCO2 response to changes in pressure support with low bias and narrow limits of agreement.

A mathematical model approach quantifying patients' response to changes in mechanical ventilation: evaluation in volume support.

This paper presents a mathematical model-approach to describe and quantify patient-response to changes in ventilator support. The approach accounts for changes in metabolism (V̇O2, V̇CO2) and serial dead space (VD), and integrates six physiological models of: pulmonary gas-exchange; acid-base chemistry of blood, and cerebrospinal fluid; chemoreflex respiratory-drive; ventilation; and degree of patients' respiratory muscle-response. The approach was evaluated with data from 12 patients on volume support ventilation mode. The models were tuned to baseline measurements of respiratory gases, ventilation, arterial acid-base status, and metabolism. Clinical measurements and model simulated values were compared at five ventilator support levels. The models were shown to adequately describe data in all patients (χ(2), p > 0.2) accounting for changes in V̇CO2, VD and inadequate respiratory muscle-response. F-ratio tests showed that this approach provides a significantly better (p < 0.001) description of measured data than: (a) a similar model omitting the degree of respiratory muscle-response; and (b) a model of constant alveolar ventilation. The approach may help predict patients' response to changes in ventilator support at the bedside.