Abstract
IntroductionThe aim of this study was to validate an automated, objective and standardized algorithm for quantifying and displaying patient-ventilator interaction.MethodsUsing a new method to detect patient-ventilator synchrony, the present study re-analyzed previously acquired and published data from 24 mechanically ventilated adult patients (Colombo et al., Crit Care Med. 2011 Nov;39(11):2452–7). Patient-ventilator interactions were evaluated by comparing ventilator pressure and diaphragm electrical activity (EAdi) waveforms, recorded during pressure support ventilation. The EAdi and ventilator pressure waveforms were analyzed for their timings (manually and automatically determined), and the error between the two waveforms was quantified. A new index of patient-ventilator interaction (NeuroSync index), which is standardized and automated, was validated and compared to manual analysis and previously published indices of asynchrony.ResultsThe comparison of manual and automated detection methods produced high test-retest and inter-rater reliability (Intraclass correlation coefficient = 0.95). The NeuroSync index increased the sensitivity of detecting dyssynchronies, compared to previously published indices, which were found to only detect asynchronies.ConclusionThe present study introduces an automated method and the NeuroSync index to determine patient-ventilator interaction with a more sensitive analysis method than those previously described. A dashboard-style of graphical display allows a rapid overview of patient-ventilator interaction and breathing pattern at the bedside.
Highlights
The aim of this study was to validate an automated, objective and standardized algorithm for quantifying and displaying patient-ventilator interaction
Data The datasets used in the present study (43 datasets in total) are from 24 adult patients with acute respiratory failure of varying etiology, intubated and on pressure support ventilation, and were obtained from previously published material from Colombo et al [4]
The present study introduces a new method for automated quantification and graphical presentation of patientventilator interaction and breathing pattern, using the electrical activity of the diaphragm (EAdi) waveform as the reference
Summary
The aim of this study was to validate an automated, objective and standardized algorithm for quantifying and displaying patient-ventilator interaction. Severe patient-ventilator asynchrony, judged by visual inspection of airway pressure and flow waveforms, can be as high as 25% in intubated and spontaneously breathing patients with acute respiratory failure, and is associated with prolonged time on mechanical ventilation and increased use of sedation [1,2,3]. It was shown that this pneumatic waveform analysis considerably underestimates the prevalence of asynchronies and may even fail to reveal whether or not the patient is breathing [4]. The diaphragm electrical activity (EAdi) waveform is a reliable signal to monitor the patient’s neural respiratory
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