An automated and standardized neural index to quantify patient-ventilator interaction

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.

Esophageal and transpulmonary pressure in the clinical setting: meaning, usefulness and perspectives

“…This difficulty justifies attempts to develop automated systems [56,57] or to use an esophageal signal, such as the electrical activity of the diaphragm (EAdi) [58] or the Pes. The Pes recording can detect asynchrony by comparing the time occurrence of the changes in Pes with those of Paw and flow-time waveforms (Fig.…”

On the Need for Standard Definitions and Education to Optimize Patient-Ventilator Interactions

“…Newer technological developments and research have demonstrated the ability to effectively detect asynchronies and dysnchronies. 4,11 This type of technology may take time to permeate to routine patient care but, given its relevance, should top the list of developments. We take this moment to caution that technology does not replace education; one without the other makes us dangerous, but both together make us more effective.…”

“…Reviews, trials, and reports use different definitions for asynchrony and its types. [2][3][4][5] This leads to confusion and hinders our ability to determine the incidence and impact of patient-ventilator interactions, their association with each mode of ventilation, and patient outcomes. One such example is the definition of double-triggering.…”

“…We will let the reader be the judge, but we could not understand it as it stands. However, a limited search of the literature yields similar definitions with different terms: "two cycles separated by a very short expiratory time, defined as less than one-half of the mean inspiratory time, the first cycle being patient-triggered" 4 or "two ventilator insufflations delivered within one patient inspiratory effort, and the first trigger should be patient-triggered." 5 Which definition is the most correct?…”

Respiratory Care Year in Review 2013: Airway Management, Noninvasive Monitoring, and Invasive Mechanical Ventilation

“…Tested on a small sample of patients, the accuracy of the computerized automatic ineffective inspiratory efforts algorithm during expiration was similar to that when compared to expert critical care staff and the electrical activity of the diaphragm. Sinderby et al 50 tested a new method of automatically detecting, quantifying, and displaying patient-ventilator interaction based on the measurements of electrical activity of the diaphragm and airway pressure waveforms. This method provides clinicians with a dashboard style of graphical display that allows a rapid overview of patient-ventilator interaction and breathing pattern.…”