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.

Patient-ventilator asynchrony during noninvasive ventilation: a bench and clinical study.

BACKGROUND: Different kinds of ventilators are available to perform noninvasive ventilation (NIV) in ICUs. Which type allows the best patient-ventilator synchrony is unknown. The objective was to compare patient-ventilator synchrony during NIV between ICU, transport-both with and without the NIV algorithm engaged-and dedicated NIV ventilators. METHODS: First, a bench model simulating spontaneous breathing efforts was used to assess the respective impact of inspiratory and expiratory leaks on cycling and triggering functions in 19 ventilators. Second, a clinical study evaluated the incidence of patient-ventilator asynchronies in 15 patients during three randomized, consecutive, 20-min periods of NIV using an ICU ventilator with and without its NIV algorithm engaged and a dedicated NIV ventilator. Patient-ventilator asynchrony was assessed using flow, airway pressure, and respiratory muscles surface electromyogram recordings. RESULTS: On the bench, frequent auto-triggering and delayed cycling occurred in the presence of leaks using ICU and transport ventilators. NIV algorithms unevenly minimized these asynchronies, whereas no asynchrony was observed with the dedicated NIV ventilators in all except one. These results were reproduced during the clinical study: The asynchrony index was significantly lower with a dedicated NIV ventilator than with ICU ventilators without or with their NIV algorithm engaged (0.5% [0.4%-1.2%] vs 3.7% [1.4%-10.3%] and 2.0% [1.5%-6.6%], P < .01), especially because of less auto-triggering. CONCLUSIONS: Dedicated NIV ventilators allow better patient-ventilator synchrony than ICU and transport ventilators, even with their NIV algorithm. However, the NIV algorithm improves, at least slightly and with a wide variation among ventilators, triggering and/or cycling off synchronization.

Neurally adjusted ventilatory assist (NAVA) improves patient-ventilator interaction during non-invasive ventilation delivered by face mask.

PURPOSE: To determine if, compared to pressure support (PS), neurally adjusted ventilatory assist (NAVA) reduces patient-ventilator asynchrony in intensive care patients undergoing noninvasive ventilation with an oronasal face mask. METHODS: In this prospective interventional study we compared patient-ventilator synchrony between PS (with ventilator settings determined by the clinician) and NAVA (with the level set so as to obtain the same maximal airway pressure as in PS). Two 20-min recordings of airway pressure, flow and electrical activity of the diaphragm during PS and NAVA were acquired in a randomized order. Trigger delay (T(d)), the patient's neural inspiratory time (T(in)), ventilator pressurization duration (T(iv)), inspiratory time in excess (T(iex)), number of asynchrony events per minute and asynchrony index (AI) were determined. RESULTS: The study included 13 patients, six with COPD, and two with mixed pulmonary disease. T(d) was reduced with NAVA: median 35 ms (IQR 31-53 ms) versus 181 ms (122-208 ms); p = 0.0002. NAVA reduced both premature and delayed cyclings in the majority of patients, but not the median T(iex) value. The total number of asynchrony events tended to be reduced with NAVA: 1.0 events/min (0.5-3.1 events/min) versus 4.4 events/min (0.9-12.1 events/min); p = 0.08. AI was lower with NAVA: 4.9 % (2.5-10.5 %) versus 15.8 % (5.5-49.6 %); p = 0.03. During NAVA, there were no ineffective efforts, or late or premature cyclings. PaO(2) and PaCO(2) were not different between ventilatory modes. CONCLUSION: Compared to PS, NAVA improved patient ventilator synchrony during noninvasive ventilation by reducing T(d) and AI. Moreover, with NAVA, ineffective efforts, and late and premature cyclings were absent.

Can neurally adjusted ventilatory assist (NAVA) improve patient-ventilator interaction? A preliminary study

INTRODUCTION. NAVA is a new spontaneous-assisted ventilatory mode based on thedetection of diaphragmatic electrical activity (Eadi) and its feedback to adjust ventilatorsettings. NAVA uses the Eadi, an expression of the respiratory center's activity, to initiatepressurization, set the level of pressure support and cycle the ventilator into exhalation.Therefore, NAVA should theoretically allow near-perfect synchronization between the patientand the ventilator. However there are few data documenting these effects in intensive carepatients.OBJECTIVES. To determine whether NAVA can improve patient-ventilator synchronycompared to standard pressure support (PS) in intubated intensive care patients.METHODS. Comparative study of patient-ventilator interaction during PS with cliniciandetermined ventilator settings and NAVA with NAVA gain (proportionality factor betweenEadi and the amount of delivered inspiratory pressure) set as to obtain the same peak airwaypressure as the total pressure obtained in PS. A 20 min continuous recording with eachventilatory mode was performed allowing determination of trigger delay (Td), patient neuralinspiratory time (Tin), duration of pressurization by the ventilator (Tiv), excess durationof pressurization (Ti excess = Tiv - Tin/Tin 9 100) and number of asynchrony events byminute: non-triggering breaths, auto-triggering, double triggering, premature and delayedcycling.Results are given in mean ± SD. p is considered significant if\0.05.RESULTS. Preliminary results (mean ± SD): five patients (age 75 ± 12 years, 1 M/4F,BMI 25.7 ± 4.1 kg m-2), two pts with COPD, 1 with restrictive disease, initial settings: PS14.6 ± 1.7 cm H2O, PEEP 6.4 ± 1.5 cm H2O, NAVA gain 2.8 ± 1.3PS NAVA % reduction NAVAversus PSTd (ms) 210.4 ± 63.0 51.8 ± 12.1* 74.5 ± 5.0Ti excess (%) 12.9 ± 19.6 2.2 ± 0.6 70.8 ± 37.8n asynchrony/minute 7.6 ± 6.4 4.1 ± 3.7* 47.5 ± 17.0Respiratory rate (min-1) 16.8 ± 2.6 20.4 ± 4.7 NA* p\0.05CONCLUSION. Compared to standard PS, NAVA improves patient ventilator interaction byreducing Td and the overall incidence of asynchrony events. There is also a strong trend inreducing delayed cycling. This ongoing trial should provide evidence that NAVA can indeedimprove patient-ventilator synchrony in intubated patients undergoing PS.REFERENCE(S). 1. Sinderby C, Navalesi P et al (1995) Neural control of mechanicalventilation in respiratory failure. Nat Med 5(12):1433-1436.2. Colombo D, Cammarota G et al (2008) Physiologic response to varying levels of pressuresupport and neurally adjusted ventilator assist in patients with acute respiratory failure.Intensive Care Med 34(11):2010-2018.

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.

Patient-ventilator asynchrony during non-invasive ventilation for acute respiratory failure: a multicenter study.

OBJECTIVE : To determine the prevalence of patient-ventilator asynchrony in patients receiving non-invasive ventilation (NIV) for acute respiratory failure. DESIGN : Prospective multicenter observation study. SETTING : Intensive care units in three university hospitals. METHODS: Patients consecutively admitted to ICU were included. NIV, performed with an ICU ventilator, was set by the clinician. Airway pressure, flow, and surface diaphragmatic electromyography were recorded continuously for 30 min. Asynchrony events and the asynchrony index (AI) were determined from visual inspection of the recordings and clinical observation. RESULTS: A total of 60 patients were included, 55% of whom were hypercapnic. Auto-triggering was present in 8 (13%) patients, double triggering in 9 (15%), ineffective breaths in 8 (13%), premature cycling 7 (12%) and late cycling in 14 (23%). An AI > 10%, indicating severe asynchrony, was present in 26 patients (43%), whose median (25-75 IQR) AI was 26 (15-54%). A significant correlation was found between the magnitude of leaks and the number of ineffective breaths and severity of delayed cycling. Multivariate analysis indicated that the level of pressure support and the magnitude of leaks were weakly, albeit significantly, associated with an AI > 10%. Patient comfort scale was higher in pts with an AI < 10%. CONCLUSION: Patient-ventilator asynchrony is common in patients receiving NIV for acute respiratory failure. Our results suggest that leaks play a major role in generating patient-ventilator asynchrony and discomfort, and point the way to further research to determine if ventilator functions designed to cope with leaks can reduce asynchrony in the clinical setting.

Effect of various neutrally adjusted ventilator assist (NAVA) gains on the relationship between diaphragmatic activity (Eadi max) and tidal volume

INTRODUCTION. Neurally Adjusted Ventilatory Assist (NAVA) is an assisted ventilatorymode in which the ventilator is driven by the electrical activity of the diaphragm (Eadi).NAVAimproves patient-ventilator synchrony [1] but little is known about how to set the NAVA gaini.e., how to choose the ratio between Eadi and delivered pressure. The aim of the present studywas to assess the relationship between Eadi and tidal volume (Vt) at various NAVA gainsettings and to evaluate whether modifying the gain influenced this relationship in non-invasivelyventilated (NIV) patients.METHODS. Prospective interventional study comparing 3 values of NAVA gain during NIV(20 min each). NAVA100 was set by the clinician according to the manufacturer's recommendations.In NAVA50 and NAVA150 the gain was set as -50% and +50% of NAVA100gain respectively. Vt and maximal Eadi value (Eadi max) were recorded. The ratio Vt/Eadi wasthen assessed for each breath. 5-95% range (range 90) of Vt/Eadi was calculated for eachpatient at each NAVA gain setting. Vt/Eadi ratio has the advantage to give an objectiveassessment Vt/Eadi max relationship independently from the nature of this relationship. Asmaller Range90 indicates a better matching of Vt to Eadi max.RESULTS. 12 patients were included, 5 had obstructive pulmonary disease and 2 mixedobstructive and restrictive disease. For NAVA100, the median [IQR] Range 90 was 32[19-87]. For NAVA150 Range 90 was 37 [20-95] and for NAVA50 Range 90 was 33 [16-92].That means that globally NAVA100 allowed a better match between Eadi max and Vt thanNAVA50 and 150. However, by patient, NAVA100 had the lowest Range 90 value for only 4patients (33%), NAVA150 for 2 (17%) and NAVA50 for 6 (50%) patients, indicating thatNAVA100 was not the best NAVA gain for minimizing Range 90 in every patients.Comparing the lowest Range 90 value to the next lowest for each patient, showed that 3 patientshad differences of less than 10% (one each for NAVA50, NAVA100 and NAVA150). Theremainder had differences from 17 to 24%, indicating that most patients (9/12 or 75%) had aclear better match between Eadi and Vt for one specific NAVA gain.CONCLUSIONS. Different NAVA gains yielded markedly different ability to match Vt toEadi max. This approach could be a new way to determine optimalNAVAgain for each patientbut require further investigations.REFERENCE. Piquilloud L, et al. Intensive Care Med 2011;37:263-71.

Automated detection of patient-ventilator asynchrony: new tool or new toy?

Although severe patient-ventilator asynchrony is frequent during invasive and non-invasive mechanical ventilation, diagnosing such asynchronies usually requires the presence at the bedside of an experienced clinician to assess the tracings displayed on the ventilator screen, thus explaining why evaluating patient-ventilator interaction remains a challenge in daily clinical practice. In the previous issue of Critical Care, Sinderby and colleagues present a new automated method to detect, quantify, and display patient-ventilator interaction. In this validation study, the automatic method is as efficient as experts in mechanical ventilation. This promising system could help clinicians extend their knowledge about patient-ventilator interaction and further improve assisted mechanical ventilation.

NAVA enhances tidal volume and diaphragmatic electro-myographic activity matching: a Range90 analysis of supply and demand.

Neurally adjusted ventilatory assist (NAVA) is a ventilation assist mode that delivers pressure in proportionality to electrical activity of the diaphragm (Eadi). Compared to pressure support ventilation (PS), it improves patient-ventilator synchrony and should allow a better expression of patient's intrinsic respiratory variability. We hypothesize that NAVA provides better matching in ventilator tidal volume (Vt) to patients inspiratory demand. 22 patients with acute respiratory failure, ventilated with PS were included in the study. A comparative study was carried out between PS and NAVA, with NAVA gain ensuring the same peak airway pressure as PS. Robust coefficients of variation (CVR) for Eadi and Vt were compared for each mode. The integral of Eadi (ʃEadi) was used to represent patient's inspiratory demand. To evaluate tidal volume and patient's demand matching, Range90 = 5-95 % range of the Vt/ʃEadi ratio was calculated, to normalize and compare differences in demand within and between patients and modes. In this study, peak Eadi and ʃEadi are correlated with median correlation of coefficients, R > 0.95. Median ʃEadi, Vt, neural inspiratory time (Ti_ ( Neural )), inspiratory time (Ti) and peak inspiratory pressure (PIP) were similar in PS and NAVA. However, it was found that individual patients have higher or smaller ʃEadi, Vt, Ti_ ( Neural ), Ti and PIP. CVR analysis showed greater Vt variability for NAVA (p < 0.005). Range90 was lower for NAVA than PS for 21 of 22 patients. NAVA provided better matching of Vt to ʃEadi for 21 of 22 patients, and provided greater variability Vt. These results were achieved regardless of differences in ventilatory demand (Eadi) between patients and modes.

Nava enhances ventilatory variability and diaphragmatic activity/tidal volume coupling

INTRODUCTION. 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. Therefore, it is expected that tidal volume (Vt) under NAVA would show better correlation with Eadi compared with PS, and exhibit greater variability due to the variability in the Eadi input to the ventilator. OBJECTIVES. To compare tidal volume variability in PS and NAVA ventilation modes, and its correlation with patient ventilatory demand (as characterized by maximum Eadi). METHODS. Acomparative study of patient-ventilator interaction was performed for 22 patients during standard PS with clinician determined ventilator settings; and NAVA, with NAVA gain set to ensure the same peak airway pressure as the total pressure obtained in PS. A 20 min continuous recording was performed in each ventilator mode. Respiratory rate, Vt, and Eadi were recorded. Tidal volume variance and Pearson correlation coefficient between Vt and Eadi were calculated for each patient. A periodogram was plotted for each ventilator mode and each patient, showing spectral power as a function of frequency to assess variability. RESULTS. Median, lower quartile and upper quartile values for Vt variance and Vt/Eadi correlation are shown in Table 1. The NAVA cohort exhibits substantially greater correlation and variance than the PS cohort. Power spectrums for Vt and Eadi are shown in Fig. 1 (PS and NAVA) for a typical patient. The enlarged section highlights how changes in Eadi are highly synchronized with NAVA ventilation, but less so for PS. CONCLUSIONS. There is greater variability in tidal volume and correlation between tidal volume and diaphragmatic electrical activity with NAVA compared to PS. These results are consistent with the improved patient-ventilator synchrony reported in the literature.

Neurally Adjusted Ventilatory Assist (NAVA) improves the matching of diaphragmatic electrical activity and tidal volume in comparison to pressure support (PS) under non invasive ventilation

INTRODUCTION. Patient-ventilator asynchrony is a frequent issue in non invasivemechanical ventilation (NIV) and leaks at the patient-mask interface play a major role in itspathogenesis. NIV algorithms alleviate the deleterious impact of leaks and improve patient-ventilator interaction. Neurally adusted ventilatory assist (NAVA), a neurally triggered modethat avoids interferences between leaks and the usual pneumatic trigger, could further improvepatient-ventilator interaction in NIV patients.OBJECTIVES. To evaluate the feasibility ofNAVAin patients receiving a prophylactic postextubationNIV and to compare the respective impact ofPSVandNAVAwith and withoutNIValgorithm on patient-ventilator interaction.METHODS. Prospective study conducted in 16 beds adult critical care unit (ICU) in a tertiaryuniversity hospital. Over a 2 months period, were included 17 adult medical ICU patientsextubated for less than 2 h and in whom a prophylactic post-extubation NIV was indicated.Patients were randomly mechanically ventilated for 10 min with: PSV without NIV algorithm(PSV-NIV-), PSV with NIV algorithm (PSV-NIV+),NAVAwithout NIV algorithm (NAVANIV-)and NAVA with NIV algorithm (NAVA-NIV+). Breathing pattern descriptors, diaphragmelectrical activity, leaks volume, inspiratory trigger delay (Tdinsp), inspiratory time inexcess (Tiexcess) and the five main asynchronies were quantified. Asynchrony index (AI) andasynchrony index influenced by leaks (AIleaks) were computed.RESULTS. Peak inspiratory pressure and diaphragm electrical activity were similar in thefour conditions. With both PSV and NAVA, NIV algorithm significantly reduced the level ofleak (p\0.01). Tdinsp was not affected by NIV algorithm but was shorter in NAVA than inPSV (p\0.01). Tiexcess was shorter in NAVA and PSV-NIV+ than in PSV-NIV- (p\0.05).The prevalence of double triggering was significantly lower in PSV-NIV+ than in NAVANIV+.As compared to PSV,NAVAsignificantly reduced the prevalence of premature cyclingand late cycling while NIV algorithm did not influenced premature cycling. AI was not affectedby NIV algorithm but was significantly lower in NAVA than in PSV (p\0.05). AIleaks wasquasi null with NAVA and significantly lower than in PSV (p\0.05).CONCLUSIONS. NAVA is feasible in patients receiving a post-extubation prophylacticNIV. NAVA and NIV improve patient-ventilator synchrony in different manners. NAVANIV+offers the best patient-ventilator interaction. Clinical studies are required to assess thepotential clinical benefit of NAVA in patients receiving NIV.