In addition, NAVA allows a more natural breathing pattern characterized by greater variability, which may also contribute to improve gas exchange [67] (see below).According to the principle of homeostasis, the closed loop that regulates PaCO2 comprises: sensors (or detectors), which selleck chem inhibitor are chemoreceptors; a controller (or comparator), which is the central respiratory command; and effectors, which are the respiratory muscles. Each component controls the next component in the loop, and the effectors change their activity (that is, adapt) to keep the PaCO2 value relatively constant. In other words, EAdi and therefore the breathing pattern must adapt to a variety of conditions to maintain PaCO2 within the normal range. Another regulatory mechanism is optimization of the work of breathing.

For example, the rate and/or the depth of breathing can be adjusted to minimize the energy expenditure at a given respiratory effort and/or to minimize the stretch on the lungs.Any strategy based on automated feedback control of ventilatory support should ideally require neural information on the lung volume, rate of lung volume change, and transpulmonary pressure, which are provided by mechanoreceptors in the lungs and chest wall. Finally, the variability and complexity of the breathing pattern are influenced by several factors, including the load-capacity relationship of the respiratory system [68-70], vagal afferent traffic to the brain [71], and the activity of the central pattern generators [72].Ventilatory activity is nonlinear in nature and exhibits chaos-like mathematical complexity [72,73].

Variability is a mathematically complex notion, often expressed using the coefficient of variation, which is the ratio of the standard deviation over the mean. However, the complexity of flow and EAdi variability can also be described using noise titration, the largest Lyapunov exponent, Kolmogorov-Sinai entropy, and three-dimensional phase portraits [74,75]. Schmidt and colleagues used these methods to compare respiratory variability and complexity during PSV and NAVA [23]. Compared with PSV, NAVA increased breathing pattern variability and flow complexity without changing EAdi complexity. Accordingly, when the NAVA level was increased from zero to a high level in healthy individuals, they adapted their inspiratory activity to the NAVA level in order to control VT and to regulate PaCO2 over a broad range of NAVA settings [63].

In contrast, during high-level PSV, VT became almost entirely determined by the ventilator and hypocapnia developed as previously shown in healthy subjects [76,77]. These differences between NAVA and PSV establish that with NAVA, even at a high level of assistance, VT is not imposed by the ventilator but remains under the control of the patient’s central Entinostat respiratory command. NAVA therefore decreases the risk of over-assistance.