Modeling the complex dynamics of derecruitment in the lung.

Recruitment maneuvers using deep inflations (DI) have long been used clinically with the objective of recruiting collapsed regions of the lung. Considerable uncertainty continues to exist, however, as to how best to employ recruitment maneuvers or even if they should be used routinely at all for patients receiving mechanical ventilation. Much of this uncertainty may arise from a lack of understanding about the dynamic nature of recruitment and derecruitment. To shed some light on this complex issue, we developed a time-dependent computational model of recruitment and derecruitment in the lung based on a symmetrically bifurcating airway tree in which each branch has a critical closing and opening pressure as well as pressure-dependent opening and closing speeds. Starting from the fully open state, the model underwent regular ventilation for 8 min followed by a series of identical DIs separated by 5 min of identical regular ventilation. We found that the geographical nature and extent of derecruitment before and 5 min after each DI were not always the same, demonstrating that the model exhibits multiple stable states. We conclude that the effectiveness of a recruitment maneuver is not only simply a function of the duration and magnitude of a DI, but may also have an unpredictable component arising from the distributed bi-stable nature of the derecruitment process itself.