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Neurally Adjusted Ventilatory Assist Increases Respiratory Variability and Complexity in Acute Respiratory Failure

Authors :
Alexandre Gharbi
Stewart B. Gottfried
Christian Straus
Marie-Noëlle Fiamma
Christophe Cracco
Matthieu Schmidt
Alexandre Duguet
Alexandre Demoule
Thomas Similowski
Neurophysiologie Respiratoire Expérimentale et Clinique
Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)
CHU Pitié-Salpêtrière [AP-HP]
Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)
Unité de soins Continus (USC)
Centre Hospitalier d'Angoulême (CH Angoulême)
Réanimation Médicale
Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Henri Mondor
Neurophysiologie Respiratoire Expérimentale et Clinique (UMRS 1158)
Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)
Source :
Anesthesiology, Anesthesiology, 2010, 112 (3), pp.670-681. ⟨10.1097/ALN.0b013e3181cea375⟩
Publication Year :
2010
Publisher :
Ovid Technologies (Wolters Kluwer Health), 2010.

Abstract

Background Neurally 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. Methods Flow 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. Results Switching 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. Conclusions Compared 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.

Details

ISSN :
00033022
Volume :
112
Database :
OpenAIRE
Journal :
Anesthesiology
Accession number :
edsair.doi.dedup.....c1ec7e249232cb116329a34d1ad5b451