• Matthieu Schmidt, Alexandre Demoule, Christophe Cracco, Alexandre Gharbi, Marie-Noëlle Fiamma, Christian Straus, Alexandre Duguet, Stewart B Gottfried, and Thomas Similowski.
• Assistance Publique-Hôpitaux de Paris, Service de Pneumologie et Réanimation, Groupe Hospitalier Pitié-Salpêtrière, and Université Paris 6-Pierre et Marie Curie, Paris, France.
• Anesthesiology. 2010 Mar 1;112(3):670-81.

BackgroundNeurally adjusted ventilatory assist (NAVA) is a partial ventilatory support mode where positive pressure is provided in relation to diaphragmatic electrical activity (EAdi). Central inspiratory activity is normally not monotonous, but it demonstrates short-term variability and complexity. The authors reasoned that NAVA should produce a more "natural" or variable breathing pattern than other modes. This study compared respiratory variability and complexity during pressure support ventilation (PSV) and NAVA.MethodsFlow and EAdi were recorded during routine PSV (tidal volume approximately 6-8 ml/kg) and four NAVA levels (1-4 cm H2O/microVEAdi) in 12 intubated patients. Breath-by-breath variability of flow and EAdi-related variables was quantified by the coefficient of variation (CV) and autocorrelation analysis. Complexity of flow and EAdi was described using noise titration, largest Lyapunov exponent, Kolmogorov-Sinai entropy, and three-dimensional phase portraits.ResultsSwitching from PSV to NAVA increased the CV and decreased the autocorrelation for most flow-related variables in a dose-dependent manner (P < 0.05, partial eta for the CV of mean inspiratory flow 0.642). The changes were less marked for EAdi. A positive noise limit was consistently found for flow and EAdi. Largest Lyapunov exponent and Kolmogorov-Sinai entropy for flow were greater during NAVA than PSV and increased with NAVA level (P < 0.05, partial eta 0.334 and 0.312, respectively). Largest Lyapunov exponent and Kolmogorov-Sinai entropy for EAdi were not influenced by ventilator mode.ConclusionsCompared with PSV, NAVA increases the breathing pattern variability and complexity of flow, whereas the complexity of EAdi is unchanged. Whether this improves clinical outcomes remains to be determined.

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