The NeuroSync index - in combination with the graphical illustration - allows an understanding of the
relative differences in timing between neural efforts and assist-delivery. The index, therefore, becomes insensitive to variances in breathing pattern which can occur with age and disease. For example, a trigger delay of 100 ms in a newborn having a neural inspiratory time of 300 ms results in a 33% error, and could be considered unacceptable. However, in an adult patient with a neural inspiratory time of 800 ms, the same trigger delay represents a 12% error and could be considered acceptable. The 33% error limits may be considered big, however, delays of this magnitude are empirically common in conventional pneumatically controlled modes[
8].
A neural inspiratory effort modulates motor-unit firing rate and recruitment of the diaphragm, whose temporo-spatial summation yields the EAdi[
5]. Hence, the EAdi signal if acquired and processed accurately represents the neural inspiratory drive to the diaphragm. The present study uses a recommended and standardized method to process EAdi[
5]. Yet, the EAdi can be disturbed by other signals such as the electrocardiograph (ECG), thus impairing accurate determination of the onset and/or end of a neural effort. For example, an ECG occurring during the onset of the neural inspiration (would be detected and replaced by its previous value) and could give a negative trigger error (airway pressure increase before EAdi increase).
The concept of a negative trigger error deserves some consideration. A negative trigger error is when the pressurization occurs prior to the onset of the EAdi signal, and can occur anywhere between the 0% and −100% error range. In our analysis, negative trigger errors that were less than −33% error (that is, 0% to −33%) were classified as synchrony. When could this possibly be observed? As mentioned above, the ECG replacement algorithm could interfere with detection of the onset of the EAdi. In the present EAdi analysis, we estimated the maximum error to determine the onset and end of a single EAdi effort to be equivalent to the duration of P- or QRS-waveforms. When averaged over hundreds of breaths, this error would become minute.
Negative trigger errors falling between the −33% and −100% error were classified as dyssynchrony. Although one could consider a significant negative trigger error as an asynchronous event, we quantified them and classified them as dyssynchrony in order to provide symmetry of our numerical and graphical representation. In the present study, these events were rare: only 0.9% of the total number of events fell into the category of negative trigger error and were classified as dyssynchrony (data not shown). Recent work by Akoumianaki
et al.[
9] in sedated and mechanically ventilated patients demonstrated respiratory entrainment, where patients’ neural efforts shortly follow ventilator pressurization. In theory, if this time delay would fall between −33 and −100% negative trigger error, it would be classified as dyssynchrony.
Naturally, extreme negative trigger errors are actually assist-without-Eadi and were classified as asynchrony, and given an error of 100%, as there is no patient interaction (no effort) associated with the ventilator.
Our analysis showed that subventilatory efforts are rare and typically related to very low EAdi amplitudes (<2 to 3 μV) and that their elimination has its greatest value at sensitive trigger/threshold levels (0.25 μV). A problem of subventilatory EAdi efforts is that if they fail to initiate assist, the event is classified as EAdi-without-assist (ineffective effort, also known as wasted effort), whereas if assist is initiated it is classified as assist-without-EAdi (auto-triggering). Subventilatory EAdi efforts introduced uncertainties in determining neural breathing pattern and with regards to the agreement between manual and automated analysis to determine FN, it was clear that low EAdi amplitude worsened the reliability. This underlines the importance of a good signal to noise ratio for automated analysis. It must be noted that very low or no EAdi signal that does not reach the EAdi threshold does not preclude that patients may be activating inspiratory muscles other than the diaphragm. The nature of these subventilatory efforts is unknown, but is most likely related to spontaneous firing or recruitment of diaphragm motor units. This is to be distinguished from signal disturbances, which are typically much higher in amplitude, and managed by other software algorithms.
Comparison of NeuroSync definitions to previous asynchrony definitions
The group of Thille
et al.[
1] was the first to describe and quantify major
asynchronies, such as wasted efforts and auto-triggering, using only airway pressure and flow waveforms, albeit without the EAdi as a reference.
The NeuroSync event defined as EAdi-without-assist corresponds to ineffective triggering with the AI
Thille. An inspiratory effort not rewarded by a ventilator breath is a failure for a triggered mode and is the asynchrony predominantly associated with adverse patient outcomes[
1,
2]. As ineffective triggering typically relates to a failure of the conventional ventilator flow and pressure sensors to detect an inspiratory effort, it is not surprising that the prevalence of ineffective triggering is greatly underestimated by airway flow and pressure detection[
4].
The NeuroSync event defined as assist-without-EAdi resembles auto-triggering with AI
Thille. If not induced by backup modes during apnea, auto-triggering is another faulty condition where the ventilator triggers and cycles off uncontrollably, and hyperventilates the patient. Auto-triggering is a very difficult asynchrony to detect with AI
Thille, because there is no true patient reference to validate the ventilator triggering[
10].
To describe
dyssynchrony, the method of Thille
et a l.[
1] involved detection of short and prolonged cycles (Table
1). Considering the natural variability in breathing, however, the significance of detecting these remains unclear[
11,
12]. The closest comparison to the NeuroSync index for short cycles would be late triggering and early cycling-off values, which would fall outside the box, that is, in the upper left quadrants in Figures
1 and
3. Long cycles (Figure
1 and Table
1) are likely to be associated with early trigger (lower quadrants) and/or delayed cycling-off (right-side quadrants) or repeated EAdi during assist (asynchrony).
The AI
Thille index[
1] also includes double-triggering, an event corresponding to multiple-assist-during-EAdi with the NeuroSync index. Multiple-assist-during-EAdi reflects repeated trigger and cycling-off errors during the same neural effort, which are classified as dyssynchrony. It should be noted that in assist-volume control, double triggering is a severe asynchrony associated with excessive tidal volumes[
13]. In non-flow and volume-regulated modes, double triggering would only cause a timing error with a short interruption of the inspiratory assist during an inspiratory effort.
The NeuroSync index also introduces another type of asynchrony labeled multiple-EAdi-during-assist, a severe type of asynchrony where the ventilator is delivering one breath for several neural inspiratory efforts. The AIThille has no counterpart for multiple-EAdi-during-assist.
Because EAdi-without-assist, assist-without-EAdi, and multiple-EAdi-during-assist all describe failures of the ventilator trigger and cycling-off functions, these events were labeled as 100% trigger error and 100% cycling-off error, and called asynchrony.
In the context of the above discussion it is important to note that AI
Colombo[
4] significantly increases the sensitivity to detect asynchrony compared to AI
Thille.
Our results show that both the AI
Thille and the EAdi-verified AI
Colombo[
4] were not designed to detect timing errors between neural effort and assist-delivery (that is, they only detect asynchrony), whereas the NeuroSync index has added the ability to also quantify dyssynchrony and synchrony. This is evidenced in Figure
2A showing that the NeuroSync index reached 40% before AI
Colombo surpassed 10%. The close association between indices when AI
Colombo exceeds 10% (Figure
2B) shows that asynchronies are detected by both indices. As evidenced by a close relationship between the asynchronous events detected with the NeuroSync index and AI
Colombo, most asynchronies defined by the NeuroSync index provide information similar to that of the pressure- and flow-based asynchrony index described by Thille
et al.[
1].
Another index of asynchrony based on EAdi was described by Beck
et al.[
14], where the sum of trigger delays and cycling-off delays (determined manually) were expressed as a percentage of the total neural respiratory cycle[
8,
14‐
16]. The NeuroSync index can be considered a development of the previously described EAdi-based index.
This new method has important clinical implications. The NeuroSync index provides real-time detection and quantification of: (i) patient ventilator asynchrony (of different types), (ii) dyssynchrony and (iii) synchrony. The method allows objective evaluation of patient neural breathing pattern and the ventilator performance, and could be used to adjust ventilator settings in order to optimize patient-ventilator interaction. Improved matching of patient and ventilator timings could enhance lung-distending pressure and ventilatory efficiency. Moreover, no study has yet shown any negative outcome of timing errors, related specifically to trigger and cycle-off delays (dyssynchrony). Asynchrony has been associated with prolonged time on mechanical ventilation[
1,
2]. The NeuroSync index, therefore, could provide a tool for future studies to determine acceptable limits of dyssynchrony. It should be pointed out that since the EAdi signal is a pneumatically independent signal, it is not affected by leaks, implying that the analysis presented could be applied reliably in intubated infants with uncuffed endotracheal tubes and during non-invasive ventilation.