Efficacy of ventilator waveform observation for detection of patient–ventilator asynchrony during NIV: a multicentre study

Discussion

Our study shows that: 1) the overall ability of ICU physicians to detect patient–ventilator asynchrony during NIV by inspection of flow and pressure waveforms and the physicians' performance in detecting AI ≥10% is low; 2) expertise off the clinician and geographic origin do not affect the rate of detection; 3) asynchrony detection is slightly, though significantly, higher with mask than with helmet; and 4) the rate of proper detection is inversely related to the prevalence of asynchrony.

To our knowledge, this is the first study that aimed to assess the ability of ICU physicians to recognise asynchronies during NIV by visual inspection of P_aw and flow–ventilator waveforms. Previous work conducted in invasively mechanically ventilated patients reports an overall ability to properly recognise patient–ventilator asynchronies that is quite low, as indicated by sensitivity for BA of 22% [14].

Patient–ventilator synchrony during partial ventilatory assistance has increasingly gained attention in the last decade. Poor patient–ventilator synchrony increases the work of breathing [20, 21] and worsens patient comfort [22, 23], which holds true also during NIV [7]. Poor comfort causes NIV intolerance, and represents one of the major determinants of NIV failure and endotracheal intubation [24], both in hypercapnic [25] and hypoxaemic [26] ARF.

Consistent with the results obtained in intubated patients [14], in the present work, asynchrony detection is inversely related to their prevalence, indicating that the chance for waveforms observation to correctly quantify asynchrony gets lower when their occurrence increases. In contrast, while clinical experience affected the ability to recognise asynchronies in the study by Colombo et al. [14], in the present study, we did not observe the same finding. On the one hand, this might suggest that 6 months of training is sufficient to reach a plateau in the learning curve. On the other hand, it might be that detection of asynchronies during NIV is extremely problematic irrespective of the level of experience, which is, in our opinion, the most likely explanation for this finding.

We also found sensitivity to be higher with a mask than a helmet, which is likely to be due to the different physical properties of the two interfaces, the helmet having larger inner volume and being more compliant. To overcome these drawbacks, the NIV settings with the helmet were adjusted according to the indications of Vargas et al. [27], who showed that increasing positive end-expiratory pressure and pressure support improves pressurisation rate and muscle unloading. It is worth remarking that, compared to the mask, the helmet was characterised by more IEs and fewer DTs. Because DTs are easier to detect than IEs because of a stronger signal on P_aw and flow tracings, this might partially explain the higher detection with a mask than a helmet.

The tracings with rates of AI ≥10% is consistent with the results of previous investigations [7–10, 15, 28, 29], while our average AI (18.1%) is slightly lower than previously reported by Vignaux et al. [7] (26%, interquartile range 15–54%). It should be noted, however, that in that study, the asynchronies were related to the extent of air leaks and all but six patients received NIV through ventilators not equipped with software for compensation of leaks [7]. Notably, the six patients ventilated with a dedicated ventilator for NIV in the study by Vignaux et al. [7] did not suffer of any asynchronous events. In our study, air leaks were contained and, in addition, we used an ICU ventilator equipped with dedicated NIV software compensating for leaks.

The results of our study are of clinical interest. In fact, as timely detection of asynchrony would lead to interface and/or ventilator settings adjustments [3, 7, 30], not recognising patient–ventilator asynchrony may affect NIV outcome. Automatic detection of asynchronies have been proposed either for invasive ventilation [31–34] or NIV [32, 35] by means of algorithms using different methodologies. Applying a noise filter and an unintentional leak compensation algorithm for IEs and DTs detection during both invasive ventilation (n=10) and NIV (n=10), Mulqueeny et al. [32] reported overall high sensitivity (94.7%) and specificity (95.1%) compared to manual assessment based on transdiaphragmatic pressure measurements. This algorithm, however, considered neither ATs, accounting for 40.5% of all asynchronies in our study, nor IEs occurring during the inspiratory phase. In 14 children with cystic fibrosis undergoing NIV, Cuvelier et al. [35] applied an algorithm using phase portraits of temporal modifications of patient–ventilator interaction and identified 94.6% of all IEs observed from oesophageal pressure tracings, without considering other asynchronies. Because of the poor performance of visual inspection of ventilator waveforms, algorithms able to recognise patient–ventilator asynchrony might indeed represent an important advance for the management of patients undergoing NIV. The preliminary data from these case series, however, need to be confirmed by further studies enrolling much higher numbers of patients.

Applying an algorithm automatically calculating an index based on the EA_di signal analysis [36] in 12 chronic obstructive pulmonary disease (COPD) patients receiving NIV for an episode of ARF, Doorduin et al. [37] recently found that IEs considerably increase when the timing errors between EA_di and P_aw reached 20% of the overall breath count, and accordingly suggested that an “acceptable” synchrony should be kept below that threshold. We have computed the sensitivity and specificity for RA, setting different cut-off to higher AI values (15%, 20% and 25%), overall and separately for the two interfaces. Increasing the AI threshold from 10% to 25% progressively worsens sensitivity while not affecting specificity (table S7). It is worth mentioning that in contrast to the study by Doorduin et al. [37], only 12.5% of our tracings include COPD patients.

Our study has strengths including the multicentre study design, the high level of experience of the involved centres, and their acquaintance with both masks and helmets. Considering the lack of differences due to the geographic origin and the high level of NIV-related expertise of the centres involves, it is reasonable assuming the rather poor performance we observed can be generalised to the vast majority of ICUs worldwide.

Our study has one major limitation. We based our study only on ventilator waveform interpretation, without the possibility to analyse additional “visual” signs (i.e. patient's respiratory rate in comparison with the ventilator rate) and parameters available at the bedside, which help the physician to recognise the mismatch between patient's spontaneous breathing and ventilator assistance. Moreover, because during NIV the patients receives no sedation at all or only small amounts of sedatives, the patient is able to communicate the discomfort arising from poor synchrony with the ventilator. To reduce this limitation as much as possible, we purposely avoided considering “minor asynchronies” in our evaluation, such as premature and delayed cycled breaths [38], taking into account only the “major asynchronies”, i.e. IEs, ATs and DTs [3, 14]. Nonetheless, it is reasonable to assume that our result would have been better including patient observation at the bedside.