Laurence Vignaux

Neurally adjusted ventilatory assist improves patient-ventilator interaction.

PurposeTo determine if, compared with pressure support (PS), neurally adjusted ventilatory assist (NAVA) reduces trigger delay, inspiratory time in excess, and the number of patient–ventilator asynchronies in intubated patients.MethodsProspective interventional study in spontaneously breathing patients intubated for acute respiratory failure. Three consecutive periods of ventilation were applied: (1) PS1, (2) NAVA, (3) PS2. Airway pressure, flow, and transesophageal diaphragmatic electromyography were continuously recorded.ResultsAll results are reported as median (interquartile range, IQR). Twenty-two patients were included, 36.4% (8/22) having obstructive pulmonary disease. NAVA reduced trigger delay (ms): NAVA, 69 (57–85); PS1, 178 (139–245); PS2, 199 (135–256). NAVA improved expiratory synchrony: inspiratory time in excess (ms): NAVA, 126 (111–136); PS1, 204 (117–345); PS2, 220 (127–366). Total asynchrony events were reduced with NAVA (events/min): NAVA, 1.21 (0.54–3.36); PS1, 3.15 (1.18–6.40); PS2, 3.04 (1.22–5.31). The number of patients with asynchrony index (AI) >10% was reduced by 50% with NAVA. In contrast to PS, no ineffective effort or late cycling was observed with NAVA. There was less premature cycling with NAVA (events/min): NAVA, 0.00 (0.00–0.00); PS1, 0.14 (0.00–0.41); PS2, 0.00 (0.00–0.48). More double triggering was seen with NAVA, 0.78 (0.46–2.42); PS1, 0.00 (0.00–0.04); PS2, 0.00 (0.00–0.00).ConclusionsCompared with standard PS, NAVA can improve patient–ventilator synchrony in intubated spontaneously breathing intensive care patients. Further studies should aim to determine the clinical impact of this improved synchrony.

Original ResearchRespiratory CareMonitoring of Noninvasive Ventilation by Built-in Software of Home Bilevel Ventilators: A Bench Study

BACKGROUND Current bilevel positive-pressure ventilators for home noninvasive ventilation (NIV) provide physicians with software that records items important for patient monitoring, such as compliance, tidal volume (Vt), and leaks. However, to our knowledge, the validity of this information has not yet been independently assessed. METHODS Testing was done for seven home ventilators on a bench model adapted to simulate NIV and generate unintentional leaks (ie, other than of the mask exhalation valve). Five levels of leaks were simulated using a computer-driven solenoid valve (0-60 L/min) at different levels of inspiratory pressure (15 and 25 cm H(2)O) and at a fixed expiratory pressure (5 cm H(2)O), for a total of 10 conditions. Bench data were compared with results retrieved from ventilator software for leaks and Vt. RESULTS For assessing leaks, three of the devices tested were highly reliable, with a small bias (0.3-0.9 L/min), narrow limits of agreement (LA), and high correlations (R(2), 0.993-0.997) when comparing ventilator software and bench results; conversely, for four ventilators, bias ranged from -6.0 L/min to -25.9 L/min, exceeding -10 L/min for two devices, with wide LA and lower correlations (R(2), 0.70-0.98). Bias for leaks increased markedly with the importance of leaks in three devices. Vt was underestimated by all devices, and bias (range, 66-236 mL) increased with higher insufflation pressures. Only two devices had a bias < 100 mL, with all testing conditions considered. CONCLUSIONS Physicians monitoring patients who use home ventilation must be aware of differences in the estimation of leaks and Vt by ventilator software. Also, leaks are reported in different ways according to the device used.

Patient-ventilator asynchrony during noninvasive pressure support ventilation and neurally adjusted ventilatory assist in infants and children.

Objectives: To document the prevalence of asynchrony events during noninvasive ventilation in pressure support in infants and in children and to compare the results with neurally adjusted ventilatory assist. Design: Prospective randomized cross-over study in children undergoing noninvasive ventilation. Setting: The study was performed in a PICU. Patients: From 4 weeks to 5 years. Interventions: Two consecutive ventilation periods (pressure support and neurally adjusted ventilatory assist) were applied in random order. During pressure support (PS), three levels of expiratory trigger (ETS) setting were compared: initial ETS (PSinit), and ETS value decreased and increased by 15%. Of the three sessions, the period allowing for the lowest number of asynchrony events was defined as PSbest. Neurally adjusted ventilator assist level was adjusted to match the maximum airway pressure during PSinit. Positive end-expiratory pressure was the same during pressure support and neurally adjusted ventilator assist. Asynchrony events, trigger delay, and cycling-off delay were quantified for each period. Results: Six infants and children were studied. Trigger delay was lower with neurally adjusted ventilator assist versus PSinit and PSbest (61 ms [56–79] vs 149 ms [134–180] and 146 ms [101–162]; p = 0.001 and 0.02, respectively). Inspiratory time in excess showed a trend to be shorter during pressure support versus neurally adjusted ventilator assist. Main asynchrony events during PSinit were autotriggering (4.8/min [1.7–12]), ineffective efforts (9.9/min [1.7–18]), and premature cycling (6.3/min [3.2–18.7]). Premature cycling (3.4/min [1.1–7.7]) was less frequent during PSbest versus PSinit (p = 0.059). The asynchrony index was significantly lower during PSbest versus PSinit (40% [28–65] vs 65.5% [42–76], p < 0.001). With neurally adjusted ventilator assist, all types of asynchronies except double triggering were reduced. The asynchrony index was lower with neurally adjusted ventilator assist (2.3% [0.7–5] vs PSinit and PSbest, p < 0.05 for both comparisons). Conclusion: Asynchrony events are frequent during noninvasive ventilation with pressure support in infants and in children despite adjusting the cycling-off criterion. Compared with pressure support, neurally adjusted ventilator assist allows improving patient–ventilator synchrony by reducing trigger delay and the number of asynchrony events. Further studies should determine the clinical impact of these findings.

Optimizing patient-ventilator synchrony during invasive ventilator assist in children and infants remains a difficult task

Objectives: To document and compare the prevalence of asynchrony events during invasive-assisted mechanical ventilation in pressure support mode and in neurally adjusted ventilatory assist in children. Design: Prospective, randomized, and crossover study. Setting: Pediatric and Neonatal Intensive Care Unit, University Hospital of Geneva, Switzerland. Patients: Intubated and mechanically ventilated children, between 4 weeks and 5 years old. Interventions: Two consecutive ventilation periods (pressure support and neurally adjusted ventilatory assist) were applied in random order. During pressure support, three levels of expiratory trigger setting were compared: expiratory trigger setting as set by the clinician in charge (PSinit), followed by a 10% (in absolute values) increase and decrease of the clinician’s expiratory trigger setting. The pressure support session with the least number of asynchrony events was defined as PSbest. Therefore, three periods were compared: PSinit, PSbest, and neurally adjusted ventilatory assist. Asynchrony events, trigger delay, and inspiratory time in excess were quantified for each of them. Measurements and Main Results: Data from 19 children were analyzed. Main asynchrony events during PSinit were autotriggering (3.6 events/min [0.7–8.2]), ineffective efforts (1.2/min [0.6–5]), and premature cycling (3.5/min [1.3–4.9]). Their number was significantly reduced with PSbest: autotriggering 1.6/min (0.2–4.9), ineffective efforts 0.7/min (0–2.6), and premature cycling 2/min (0.1–3.1), p < 0.005 for each comparison. The median asynchrony index (total number of asynchronies/triggered and not triggered breaths ×100) was significantly different between PSinit and PSbest: 37.3% [19–47%] and 29% [24–43%], respectively, p < 0.005). With neurally adjusted ventilatory assist, all types of asynchrony events except double-triggering and inspiratory time in excess were significantly reduced resulting in an asynchrony index of 3.8% (2.4–15%) (p < 0.005 compared to PSbest). Conclusions: Asynchrony events are frequent during pressure support in children despite adjusting the cycling off criteria. Neurally adjusted ventilatory assist allowed for an almost ten-fold reduction in asynchrony events. Further studies should determine the clinical impact of these findings.

Automated detection of asynchrony in patient-ventilator interaction

An automated classification algorithm for the detection of expiratory ineffective efforts in patient-ventilator interaction is developed and validated. Using this algorithm, 5624 breaths from 23 patients in a pulmonary ward were examined. The participants (N = 3D23) underwent both conventional and non-invasive ventilation. Tracings of patient flow, pressure at the airway, and transdiaphragmatic pressure were manually labeled by an expert. Overall accuracy of 94.5% was achieved with sensitivity 58.7% and specificity 98.7%. The results demonstrate the viability of using pattern classification techniques to automatically detect the presence of asynchrony between a patient and their ventilator.

Neonatal and Adult ICU Ventilators to Provide Ventilation in Neonates, Infants, and Children: A Bench Model Study

BACKGROUND: Using a bench test model, we investigated the hypothesis that neonatal and/or adult ventilators equipped with neonatal/pediatric modes currently do not reliably administer pressure support (PS) in neonatal or pediatric patient groups in either the absence or presence of air leaks. METHODS: PS was evaluated in 4 neonatal and 6 adult ventilators using a bench model to evaluate triggering, pressurization, and cycling in both the absence and presence of leaks. Delivered tidal volumes were also assessed. Three patients were simulated: a preterm infant (resistance 100 cm H2O/L/s, compliance 2 mL/cm H2O, inspiratory time of the patient [TI] 400 ms, inspiratory effort 1 and 2 cm H2O), a full-term infant (resistance 50 cm H2O/L/s, compliance 5 mL/cm H2O, TI 500 ms, inspiratory effort 2 and 4 cm H2O), and a child (resistance 30 cm H2O/L/s, compliance 10 mL/cm H2O, TI 600 ms, inspiratory effort 5 and 10 cm H2O). Two PS levels were tested (10 and 15 cm H2O) with and without leaks and with and without the leak compensation algorithm activated. RESULTS: Without leaks, only 2 neonatal ventilators and one adult ventilator had trigger delays under a given predefined acceptable limit (1/8 TI). Pressurization showed high variability between ventilators. Most ventilators showed TI in excess high enough to seriously impair patient-ventilator synchronization (> 50% of the TI of the subject). In some ventilators, leaks led to autotriggering and impairment of ventilation performance, but the influence of leaks was generally lower in neonatal ventilators. When a noninvasive ventilation algorithm was available, this was partially corrected. In general, tidal volume was calculated too low by the ventilators in the presence of leaks; the noninvasive ventilation algorithm was able to correct this difference in only 2 adult ventilators. CONCLUSIONS: No ventilator performed equally well under all tested conditions for all explored parameters. However, neonatal ventilators tended to perform better in the presence of leaks. These findings emphasize the need to improve algorithms for assisted ventilation modes to better deal with situations of high airway resistance, low pulmonary compliance, and the presence of leaks.

Online estimation of respiratory mechanics in non-invasive pressure support ventilation: A bench model study

An online algorithm for determining respiratory mechanics in patients using non-invasive ventilation (NIV) in pressure support mode was developed and embedded in a ventilator system. Based on multiple linear regression (MLR) of respiratory data, the algorithm was tested on a patient bench model under conditions with and without leak and simulating a variety of mechanics. Bland-Altman analysis indicates reliable measures of compliance across the clinical range of interest (±11–18% limits of agreement). Resistance measures showed large quantitative errors (30–50%), however, it was still possible to qualitatively distinguish between normal and obstructive resistances. This outcome provides clinically significant information for ventilator titration and patient management.

Varying Leaks: A Challenge for Modern Ventilators?

Using patient-triggered ventilation, during which the patient is given the opportunity to trigger and cycle the ventilator, allows reducing the use of sedation,[1][1] reduces ventilator-induced diaphragmatic dysfunction,[2][2] facilitates weaning from mechanical ventilation,[3][3] and improves

Patient-Ventilator Synchrony and Tidal Volume Variability using NAVA and Pressure Support Mechanical Ventilation Modes

Abstract Neurally Adjusted Ventilatory Assist (NAVA) is a new ventilatory mode in which ventilator settings are adjusted based on the electrical activity detected in the diaphragm (Eadi). This mode offers significant advantages in mechanical ventilation over standard pressure support (PS) modes, since ventilator input is determined directly from patient ventilatory demand. A comparative study of 22 patients undergoing mechanical ventilation in both PS and NAVA modes was conducted, and it was concluded that for a given variability in Eadi, there is greater variability in tidal volume and correlation between the tidal volume and the diaphragmatic electrical activity with NAVA compared to PS. These results are consistent with the improved patient-ventilator synchrony reported in the literature.