Abstract
Background: Static driving pressure (∆P), which represents the ratio between tidal volume and respiratory system compliance, provides a surrogate for the lung’s ability to accept a given tidal volume and has been independently associated with mortality. An alternative proposal suggests using dynamic ∆P as a reliable outcome signal. It is important to recognize that dynamic parameters may be impacted by variables that affect resistance, such as iatrogenic airway resistance and inspiratory flow profile. Dynamic ∆P may also be impacted by the patient’s effort which may overestimate patient lung compliance. Exploring the specific contribution each of these variables has on dynamic ∆P and the potential variance it may have with static ∆P can aid in determining the reliability of dynamic ∆P outcome signaling.
Methods: A mathematical lung model (SIVA) and a lung simulator (ASL 5000, Ingmar Medical Pittsburg, PA) were used as bench models for this study. Fixed ventilator settings were: VC-CMV, frequency 14 breaths/min, VT 400 mL, and PEEP 10 cm H2O. Independent variables include a combination of lung model compliance (35 and 50 mL/cm H2O), lung model resistance (10, 15, and 20 cm H2O/L/s), patient inspiratory muscle effort (0, 3, 6, and 9 cm H2O), square ramp and descending ramp, and inspiratory flow (30, 40, 50, 60, and 70 L/min).
Results: In a passively ventilated patient with a square inspiratory flow profile, when airway resistance and inspiratory flow delivery increase, dynamic ∆P will increase while static ∆P will remain constant (Figure 1). The degree of difference will be reduced with the application of a descending ramp inspiratory flow profile but remains clinically significant at higher inspiratory flows. (Figure 2). During constant lung model resistance and compliance conditions, static ∆P remains constant while dynamic ∆P has a linear increase when inspiratory flow is increased. Increasing muscle pressure can influence dynamic ∆P to the degree that it will display a lower driving pressure than static ∆P. This anomaly can be resolved with the adjustment of inspiratory flow to mitigate flow starvation.
Conclusions: Static DP has a constant and reliable reporting signal during passive and active patient conditions. This level of reliability is also demonstrated with changes in lung model resistance and compliance and with increased patient effort. The opinion of dynamic ∆P as an outcome signal should be used with caution. Dynamic ∆P measurements are influenced by numerous variables that do not affect static ∆P.
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