Inverse modeling supports quantification of pressure and time depending effects in ARDS patients

摘要

The application of respiratory mechanics models combined with standardized ventilation maneuvers enable investigations of patients' lung mechanics at the bedside in order to optimize ventilation therapy. Therefore, the underlying dynamic effects of respiratory mechanics (viscoelasticity, inhomogeneity and recruitment) are uncovered by applying various ventilation maneuvers and subsequently captured by the corresponding model via parameter identification methods. Data sets of patients undergoing quasi-static and dynamic ventilation patterns are available along with a hierarchical model structure for parameter identification and simulation purposes. The applicability of the basic 1^st order model (FOM) of respiratory mechanics for various flow rates proved to be critical and patient dependent, since distinctive time-depending effects could not be considered. To improve this, a 2^nd order model (SOM), individualized using data of a SCASS maneuver (Static Compliance Automated Single Step), enables successful simulations of respiratory mechanics in dynamic and quasi-static conditions. Pressure dependent effects such as static recruitment, can be captured by Hickling's nonlinear compliance model. This research illustrates the applicability of various models of respiratory mechanics within the model hierarchy in various circumstances and the ability to distinguish between dynamic and static effects.