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Background: Subject-ventilator asynchrony (SVA) was shown to be associated with negative clinical outcomes. To elucidate pathophysiology pathways and effects of SVA on lung tissue histology a reproducible animal model of artificially induced asynchrony was developed and evaluated. Methods: Alterations in ventilator parameters were used to induce the three main types of asynchrony: ineffective efforts (IE), auto-triggering (AT), and double-triggering (DT). Airway flow and pressure, as well as oesophageal pressure waveforms, were recorded, asynchrony cycles were manually classified and the asynchrony index (AIX) was calculated. Bench tests were conducted on an active lung simulator with ventilator settings altered cycle by cycle. The developed algorithm was evaluated in three pilot experiments and a study in pigs ventilated for twelve hours with AIX = 25%. Results: IE and AT were induced reliably and fail-safe by end-expiratory hold and adjustment of respiratory rate, respectively. DT was provoked using airway pressure ramp prolongation, however not controlled specifically in the pilots. In the subsequent study, an AIX = 28.8% [24.0%-34.4%] was induced and maintained over twelve hours. Conclusions: The method allows to reproducibly induce and maintain three clinically relevant types of SVA observed in ventilated patients and may thus serve as a useful tool for future investigations on cellular and inflammatory effects of asynchrony. [ABSTRACT FROM AUTHOR]

Abstract Background Subject-ventilator asynchrony (SVA) was shown to be associated with negative clinical outcomes. To elucidate pathophysiology pathways and effects of SVA on lung tissue histology a reproducible animal model of artificially induced asynchrony was developed and evaluated. Methods Alterations in ventilator parameters were used to induce the three main types of asynchrony: ineffective efforts (IE), auto-triggering (AT), and double-triggering (DT). Airway flow and pressure, as well as oesophageal pressure waveforms, were recorded, asynchrony cycles were manually classified and the asynchrony index (AIX) was calculated. Bench tests were conducted on an active lung simulator with ventilator settings altered cycle by cycle. The developed algorithm was evaluated in three pilot experiments and a study in pigs ventilated for twelve hours with AIX = 25%. Results IE and AT were induced reliably and fail-safe by end-expiratory hold and adjustment of respiratory rate, respectively. DT was provoked using airway pressure ramp prolongation, however not controlled specifically in the pilots. In the subsequent study, an AIX = 28.8% [24.0%-34.4%] was induced and maintained over twelve hours. Conclusions The method allows to reproducibly induce and maintain three clinically relevant types of SVA observed in ventilated patients and may thus serve as a useful tool for future investigations on cellular and inflammatory effects of asynchrony.

Patient–ventilator dyssynchrony is a mismatch between the patient’s respiratory efforts and mechanical ventilator delivery. Dyssynchrony can occur at any phase throughout the respiratory cycle. There are different types of dyssynchrony with different mechanisms and different potential management: trigger dyssynchrony (ineffective efforts, autotriggering, and double triggering); flow dyssynchrony, which happens during the inspiratory phase; and cycling dyssynchrony (premature cycling and delayed cycling). Dyssynchrony has been associated with patient outcomes. Thus, it is important to recognize and address these dyssynchronies at the bedside. Patient–ventilator dyssynchrony can be detected by carefully scrutinizing the airway pressure–time and flow–time waveforms displayed on the ventilator screens along with assessing the patient’s comfort. Clinicians need to know how to depict these dyssynchronies at the bedside. This review aims to define the different types of dyssynchrony and then discuss the evidence for their relationship with patient outcomes and address their potential management.

The article assesses the ability of intensive care unit doctors to identify patient-ventilator asynchronies through ventilator waveforms. Results show that the overall ability of intensive care unit physicians to recognize patient-ventilator asynchronies was low and it decreases as prevalence gets higher. Expertise was shown to significantly increase sensitivity for breath by breath analysis, although this was considered a contributor only to improving report analysis.

Objective: Imperfect patient-ventilator interaction is common during assisted ventilation, and the detection of clinically relevant mismatching requires visual monitoring of the ventilator screen. We have assessed the feasibility, sensitivity and specificity of an algorithm embedded in a ventilator system that is able to automatically detect the occurrence of ineffective triggering and double triggering in real time.Design: Prospective study.Setting: Respiratory intensive care unit.Methods: Twenty patients undergoing pressure-support ventilation, either non-invasively (NIV, n=10) or conventionally ventilated (n=10), were studied.Measurements: The detection of ineffective triggering and double triggering from the algorithm was compared by two operators with the "real" occurrence of the phenomena as assessed using the transdiaphragmatic pressure (Pdi).Results: Seven of the 20 patients exhibited gross mismatching, while in the remaining patients patient-ventilator mismatching was artificially induced using a pressure control, with a low respiratory rate. Ineffective triggering and double triggering were identified by the operators in 507 and 19 of the 3343 analyzed breaths, respectively. False positives were significantly more frequent in the NIV group than with conventional ventilation. The algorithm had an overall sensitivity of 91% and specificity of 97%. Specificity was statistically higher in the conventional ventilated group than with NIV (99% vs. 95%, p<0.05).Conclusions: We have demonstrated the feasibility and efficacy of a new algorithm to detect the occurrence of impaired patient-ventilator interaction during mechanical ventilation in real time. This software may help the clinician in the identification of this problem, which has been shown to have important clinical consequences. [ABSTRACT FROM AUTHOR]

Objective: To evaluate a new approach for monitoring and improving patient-ventilator interaction that utilizes a signal generated by the equation of motion, using improvised values for resistance and elastance obtained noninvasively.Design and Setting: Observational study in intensive care units in five European centers.Patients: We studied 21 stable patients instrumented with esophageal/gastric catheters for a previous study and ventilated alternately with pressure support (PSV) and proportional assist (PAV) ventilation with a Tyco 840 ventilator.Measurements and Results: Previously recorded digital files were analyzed in real-time by a prototype incorporating the new technology (PVI monitor, YRT, Winnipeg, Canada). Actual onsets (P(DI)-T(ONSET)) and ends (P(DI)-T(END)) of inspiratory efforts, ineffective efforts, and patient respiratory rate were identified visually from transdiaphragmatic or calculated respiratory muscle pressure. Monitor-identified T(ONSET) occurred 0.107 +/- 0.074 s after P(DI)-T(ONSET), substantially less than trigger delay observed with conventional triggering (0.326 +/- 0.086 s). End of effort was identified 0.097 +/- 0.096 s after P(DI)-T(END), significantly less than actual cycling-off delay during PSV (0.486 +/- 0.307 s) or PAV (0.277 +/- 0.084 s). The monitor detected 80% of ineffective efforts. There was excellent agreement between monitor-estimated respiratory rate and actual patient rate over a wide range (17-59/min) of patient rates (mean (+/- SD) of difference -0.2 +/- 1.9/min for pressure support and 0.2 +/- 0.9/min for proportional assist) even when large discrepancies existed (> 35/min) between patient and ventilator rates.Conclusions: The proposed approach should make it possible to improve patient-ventilator interaction and to obtain accurate estimates of true patient respiratory rate when there is nonsynchrony. [ABSTRACT FROM AUTHOR]

Objective: To examine patient-ventilator interaction during pressure support ventilation in critically ill patients when they were ventilated: (1) by a new system (Vision, Respironics) which uses the flow waveform as a method of triggering and cycling; and (2) by a new generation ventilator (Evita 4, Drager) which uses the traditional flow triggering (2 l/min) and cycling criterion (25% of peak flow).Design: Prospective clinical and bench study.Methods: Twelve mechanically ventilated patients were studied at three levels of pressure support, applied randomly with both ventilators. The two systems of triggering were further studied at controlled levels of dynamic hyperinflation and respiratory drive using an active lung model.Results: Patients' breathing patterns, respiratory effort, and arterial blood gases were not affected by the type of ventilator. The flow waveform method of triggering was more sensitive to patient effort than the flow triggering, resulting in less ineffective effort but a greater number of auto-triggerings. At controlled levels of dynamic hyperinflation and inspiratory effort the simulated patient effort needed to trigger the ventilator was considerably less with the flow waveform method of triggering than that with the flow triggering. The flow waveform method of cycling resulted in mechanical breaths with similar characteristics to those that used the traditional flow criterion of breath termination.Conclusions: We conclude that the flow waveform method of triggering improves the ventilator function and decreases the patient effort during the triggering phase. This system is highly sensitive, but under certain circumstances may be unstable. [ABSTRACT FROM AUTHOR]

Patient–ventilator dyssynchrony is a mismatch between the patient's respiratory efforts and mechanical ventilator delivery. Dyssynchrony can occur at any phase throughout the respiratory cycle. There are different types of dyssynchrony with different mechanisms and different potential management: trigger dyssynchrony (ineffective efforts, autotriggering, and double triggering); flow dyssynchrony, which happens during the inspiratory phase; and cycling dyssynchrony (premature cycling and delayed cycling). Dyssynchrony has been associated with patient outcomes. Thus, it is important to recognize and address these dyssynchronies at the bedside. Patient–ventilator dyssynchrony can be detected by carefully scrutinizing the airway pressure–time and flow–time waveforms displayed on the ventilator screens along with assessing the patient's comfort. Clinicians need to know how to depict these dyssynchronies at the bedside. This review aims to define the different types of dyssynchrony and then discuss the evidence for their relationship with patient outcomes and address their potential management. [ABSTRACT FROM AUTHOR]

Introduction: High-intensity noninvasive ventilation (NIV) has been shown to improve outcomes in stable chronic obstructive pulmonary disease patients. However, there is insufficient knowledge about whether with this more controlled ventilatory mode optimal respiratory muscle unloading is provided without an increase in patient-ventilator asynchrony (PVA)., Patients and Methods: Ten chronic obstructive pulmonary disease patients on home mechanical ventilation were included. Four different ventilatory settings were investigated in each patient in random order, each for 15 min, varying the inspiratory positive airway pressure and backup breathing frequency. With surface electromyography (EMG), activities of the intercostal muscles, diaphragm, and scalene muscles were determined. Furthermore, pressure tracings were derived simultaneously in order to assess PVA., Results: Compared to spontaneous breathing, the most pronounced decrease in EMG activity was achieved with the high-pressure settings. Adding a high breathing frequency did reduce EMG activity per breath, while the decrease in EMG activity over 1 min was comparable with the high-pressure, low-frequency setting. With high backup breathing frequencies less breaths were pressure supported (25% vs 97%). PVAs occurred more frequently with the low-frequency settings ( P =0.017)., Conclusion: High-intensity NIV might provide optimal unloading of respiratory muscles, without undue increases in PVA., Competing Interests: MLD is the recipient of an European Respiratory Society Long-term Fellowship (LTRF 2014-3126). LAvE did assist with the measurements; however, his company (Inbiolab B.V.) did not have any influence on the design, conduction, or final analyses on the study. PJW has received a research grant and personal speaking fees from Philips/Respironics, the Netherlands. The other authors report no conflicts of interest in this work.