Post 2: Timing of assist
As mentioned in my previous blog on patient ventilator interaction: “Patient ventilator interaction has generally two dimensions: timing of assist (temporal interaction) and magnitude of assist (interaction in space), both in relation to patient effort. Temporal interaction relates to synchronization of assist delivery to inspiratory effort (i.e. ideally a simultaneous start and termination of inspiratory effort and assist). Interaction in space relates to synchronization of patient inspiratory effort to magnitude of ventilator assist (i.e. proportionality between inspiratory effort and magnitude of assist).” (Post 1: Proportionality and NAVA level).
The temporal interaction between patient effort and assist delivery has more or less dominated the topic on patient-ventilator interaction since the 1960’s (Gunaratna Brit Med J 1965). Important questions to ask when evaluating triggering during pneumatic modes is how to monitor patient effort (Ward et al Anesthesiology 1988), which mode to use for spontaneous breathing, how does the level of assist affect triggering (Leung et al Am J Respir Crit Care Med 1997), and whether to use flow or pressure triggering for partial ventilatory assist (Aslanian et al Am J Respir Crit Care Med 1998).
Inherent to all triggering, is that a more sensitive trigger results in increased risk of auto-triggering and a less sensitive trigger results in increased probability of ineffective efforts.
Triggering of assist with EAdi is based on an EAdi deflection/increase typically 0.5 mV. Monitoring neural effort is inherent to the EAdi and in NAVA, the level of assist, severity of ARF, sedation, and leaks have little influence on trigger accuracy.
The implementation of flow to cycle-off assist was introduced to allow termination of assist to be synchronized to the end of patient effort. The rationale behind the introduction of flow to terminate assist has to my knowledge never been provided.
Numerous studies – early (Van de Graff et al Chest 1991) as well as recent work (Colombo et al Intensive Care Med 2008, Spahija et al Crit Care med 2010, Patroniti et al Intensive Care Med 2012) – has shown that increased pressure support levels changes both tidal volume and respiratory rate when using flow as the cycling-off algorithm. Clinically these changes in breathing pattern have been and still are used to titrate assist levels in pressure support – lower respiratory rate being preferred. It is a fact that the flow return to a fixed value or to a percentage of its peak is delayed with increasing pressure support. It is also a fact that delayed cycling-off – i.e. a prolongation of the assist into neural exhalation prolongs the exhalation period and thus lowers respiratory rate but may not fully compensate for dynamic hyperinflation (Younes et al Am Respir Crit Care Med 2002). Another fact is that using the early decline in EAdi to cycle-off assist eliminates the prolongation of assist into neural exhalation, and thus avoids the respiratory rate depressing stimulation via the Hering Breuer “expiratory-promoting” reflex.
When NAVA levels are initially low, increasing the NAVA level typically result in reduced respiratory rate and increase volume. With continued increase in the NAVA level, as soon as NAVA provides sufficient assist, the breathing pattern will not change with further increased levels, even though EAdi and patient efforts will continue to decrease (Colombo et al Intensive Care Med 2008, Brander et al Chest 2009, Patroniti et al Intensive Care Med 2012).
Apart from the delayed cycling-off with increasing (flow-cycled) pressure support, changes in respiratory mechanics will affect the cycling-off with flow based algorithms. For example, patients with obstructive lung diseases will have a delayed termination of assist relative to those with a restrictive respiratory disorder. To avoid a delayed termination of assist and dynamic hyperinflation, recommendations are to use a more sensitive flow cycling-off criteria in obstructive patients (Tassaux et al Am J Respir Crit Care Med 2005, Chiumello et al Crit Care Med 2007). To avoid premature termination of assist a less sensitive flow cycling-off criteria (i.e. later cycling-off) has been recommended in restrictive patients (Chiumello et al Crit Care Med 2003, Mauri et al Intensive Care Med 2013).
Patient-ventilator interaction during pneumatic triggering of conventional modes of assist has been extensively studied by using so called waveform analysis, i.e. monitoring the pressure-flow-volume tracings on the ventilator.
Most studies of waveform analysis to detect temporal asynchrony reports on detection of;
- ineffective triggering, i.e. breath attempts of the patient that are not rewarded with assist and
- auto-triggered assist, i.e. assist is delivered in absence of patient inspiratory effort.
Ineffective triggering is a fault condition of the ventilator since it will a) deprive the patient of a breath and b) report erroneous information of the patient’s respiratory rate. Thille et al (Intensive Care Med 2006) showed that one quarter of intubated patients had severe asynchrony and this was associated with prolonged time of mechanical ventilation. They deducted that “excessive assist” was a cause of ineffective triggering. DeWit et al (Crit Care Med 2009) showed high incidence of ineffective triggering in intubated patients and an association to increased morbidity, longer duration of mechanical ventilation. Asynchrony was recently reported as common in intubated trauma-patients, albeit, not associated with prolonged duration of ventilation and hospitalization (Branson et al Respir Care 2013).
Excessive assist: A major problem with conventional modes of partial ventilatory assist is that they can provide excessive assist – abolishing the respiratory drive – without warning (Colombo et al Crit Care Med 2011, Ducharme-Crevier et al Crit Care Res Pract 2013). If this is an asynchrony or a fault can be discussed, regardless the consequences of “uncontrolled” excessive assist may result in serious impairment of the diaphragm function (Levine et al N Engl J Med 2008, Jaber et al Am J Respir Crit Care Dis 2011, Grosu et al Chest 2012, Picard et al Am J Respir Crit care Med 2012). Recently, Delisle et al (Respir Care 2013) associated excessive assist with central apneas during sleep in mechanically ventilated patients.
Comfort: “Patient-fighting the ventilator” – a typical description of poor patient ventilator interaction – has both subjectively (Calderini et al Intensive Care Med 1999) and objectively (de la Oliva et al Intensive Care Med 2012) been associated with discomfort as well as associated with cerebral cortex activation (Raux et al Anesthesiology 2007). Improved temporal and spatial synchrony can improve sleep quality compared to pressure support (Bosma et al Crit Care Med 2007, Delisle et al Ann Intensive Care 2011) however results are inconsistent (Alexopoulou et al Intensive Care Med 2013). In some studies assist control ventilation has been shown to improve sleep when compared to pressure support (Toublanc et al Intensive Care Med 2007, Parthasarathy et al Am J Respir Crit Care 2002). Authors suggested that sleep disturbances due to central apnea induced by pressure support may have played a role (Parthasarathy et al Am J Respir Crit Care 2002).
The conventional clinical approach to controlling a patient fighting the ventilator (i.e. manage poor patient ventilator interaction) is by sedation (Roberts et al Drugs 2012). Sedation has the effect of not only calming the patient but also the respiratory drive. De Wit et al (J Crit Care 2009) showed that deeper sedation is a predictor of ineffective triggering. Vaschetto et al (Crit Care Med 2013) recently showed that deep sedation increases ineffective triggering during pressure support, but not during NAVA. The impact of high sedation, including: duration on mechanical ventilation, hospital stay, and mortality, has been demonstrated and discussed (Roberts et al Drugs 2012). Novel approaches to improve outcome includes reduced sedation and early mobilization (Morandi et al Curr Opin Crit Care 2011, Kress Crit Care Clin 2013). Approaches range from daily wake-up trials to no sedation (Strom & Toft Minerva Anestesiol 2011).
Hence, patient-ventilator interaction is becoming a crucial link in the cogwheel for rehabilitating patients in the critical care.
Despite very high prevalence of asynchrony: studies of non-invasive ventilation have not reported adverse effects of asynchrony (Vignaux et al Intensive Care Med 2009). To speculate, less sedation during non-invasive ventilation may explain why duration of mechanical ventilation is not related to severity of asynchrony. In fact, adverse factors like intubation or re-intubation may rather increase with asynchrony in non-invasively ventilated patients.
Detection of patient-ventilator interaction
Several methods to visually or automatically detect patient-ventilator asynchrony on pressure, flow, and/or volume waveforms have been reported during many years and repeatedly summarized in reviews (Nilsestuen & Hargett Respir Care 2005, Georgopoulos et al Intensive Care Med 2006, Branson Resp Care 2011, Blanch et al Minerva Anestesiol 2013). It should be noted that these methods mainly pertain to severe asynchronies and that clinicians have low ability to recognize the asynchronies (Colombo et al Crit Care Med 2011). Moreover, pneumatic signals are poor detectors of neural respiratory drive (Parthasarathy et al Am J Respir Crit Care Med 2000, Colombo et al Crit Care Med 2011, Sinderby & Beck Patient-Ventilator Interactions, Encyclopedia of Intensive Care Medicine Eds. Vincent & Hall, Springer 2012, Ducharme-Crevier et al Crit Care Res Pract 2013). In fact, pneumatic monitoring does not allow confirmation of patient-ventilator synchrony.
Test your skills in waveform analysis
To test your skills in waveform analysis you can perform the same test of waveform analysis that was published by Colombo et al (Crit Care Med 2011).
The test provides 43 examples of flow and pressure, each of 5 minutes duration, in mechanically ventilated patients, where you can detect “Inneffective efforts (triggering)”, “Auto-triggering”, and “Double-triggering”. Just place the cursor over each asynchrony event that you detect – double-click – and select the type of asynchrony you detected. Since the analysis of each patient case takes some time, make sure to do this when you are not in a hurry. You can do one subject at the time and you can exit and re-enter any analysis as long as you do “NOT” hit the “SAVE” button. Since the “SAVE” button will close the analysis, please make sure that you do not hit the “SAVE” button unless you are sure you found ALL asynchronies and are ready to finish the exercise. Once you hit “SAVE” you will be able to confirm your findings against the EAdi signal. This is an important learning experience for the person interested in mechanical ventilation, so please take your time, be patient, and try to finish all cases diligently.
To start the waveform analysis test go to:
Register and enjoy!
How to improve patient-ventilator interaction?
As increasing sedation worsens asynchrony, the only consistently reported remedy to improve patient-ventilator interaction on conventional ventilator modes is “markedly reducing ventilatory assist” (Thille et al Intensive Care Med 2008, Spahija et al Crit Care med 2010), although this does not ensure synchrony, nor may it be tolerated by the patient without high sedation and ensure adequate alveolar ventilation. Adjustments of pressure inspiratory time, pressure rise-time, trigger and cycling-off algorithms are available on all ICU-ventilators, however, lack of adequate dys-synchrony monitoring makes their use subjective and I may dare to suggest that the manufacturers default settings are most commonly used.
Many studies, too numerous to be listed (search PubMed), has shown that NAVA improves patient-ventilator interaction during both invasive and non-invasive ventilatory assist in all age-groups. Details of the advantages will be discussed in future blogs.