The online version of this article (doi:10.1186/cc10309) contains supplementary material, which is available to authorized users.
GG has submitted a patent application based on the method described in the manuscript. All other coauthors claim no competing interests.
GG conceived of the study, participated in its design and coordination, performed the statistical analysis and was the main author of the manuscript. GJB participated in the design of the study, collection and analysis of data, and helped to draft the manuscript. HT and JA participated in the design of the study and helped to draft the manuscript. JA and VJ participated in the design of the study, collection of data and helped to draft the manuscript. LDLC and SG participated in the design of the study and collection of data. CE and BG participated in the collection and analysis of data and helped draft the manuscript. All authors have read and approved the final manuscript.
Adequate ventilatory support of critically ill patients depends on prompt recognition of ventilator asynchrony, as asynchrony is associated with worse outcomes.
We compared an automatic method of patient-ventilator asynchrony monitoring, based on airway flow frequency analysis, to the asynchrony index (AI) determined visually from airway tracings.
This was a prospective, sequential observational study of 110 mechanically ventilated adults. All eligible ventilated patients were enrolled. No clinical interventions were performed. Airway flow and pressure signals were sampled digitally for two hours. The frequency spectrum of the airway flow signal, processed to include only its expiratory phase, was calculated with the Cooley-Tukey Fast Fourier Transform method at 2.5 minute intervals. The amplitude ratio of the first harmonic peak (H1) to that of zero frequency (DC), or H1/DC, was taken as a measure of spectral organization. AI values were obtained at 30-minute intervals and compared to corresponding measures of H1/DC.
The frequency spectrum of synchronized patients was characterized by sharply defined peaks spaced at multiples of mean respiratory rate. The spectrum of asynchronous patients was less organized, showing lower and wider H1 peaks and disappearance of higher frequency harmonics. H1/DC was inversely related to AI (n = 110; r2 = 0.57; P < 0.0001). Asynchrony, defined by AI > 10%, was associated H1/DC < 43% with 83% sensitivity and specificity.
Spectral analysis of airway flow provides an automatic, non-invasive assessment of ventilator asynchrony at fixed short intervals. This method can be adapted to ventilator systems as a clinical monitor of asynchrony.
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- Automatic detection of patient-ventilator asynchrony by spectral analysis of airway flow
Guillermo J Ballarino
Lucy De La Cruz
- BioMed Central
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