TABLE 1
Modalities of detection of asynchronies
Modalities Advantages Disadvantages
Ventilator waveforms Non-invasive
Easily available Needs expertise
No automated analysis from
ventilators
Might miss some
Electrical activity from
the diaphragm Depicts diaphragm activity
Reliable
Continuous and quantifi ed
Can be mounted on feeding tubes Need a dedicated catheter and
ventilator
Does not detect other muscles
Needs proper placement
Synchrony not automatically detected
Oesophageal (and/or
transdiaphragmatic)
pressure Good estimate of the inspiratory eff ort
Gold standard
Can be mounted on feeding tubes Needs catheter placement and
specifi c monitoring
Technical specifi cities (calibration
procedure)
Software Continuous
Automatic
Real-time analysis Few studies
Not yet validated
From reference 10
Phase
Asynchronies can be classifi ed as inspiratory or
expiratory, depending on the neural respiratory
phase that is affected. Inspiratory asynchronies
are delayed triggering, ineffective effort and
autotriggering, whilst expiratory asynchronies are
late and early cycling and double triggering.
Relevance
Asynchronies can be classifi ed in major or minor,
depending on the type of assistance provided by
the ventilator: if there is no correspondence at all
between patient’s request and ventilator
assistance (that is, the patient starts a breath but
the ventilator does not provide any support), the
asynchrony is ‘major’, whereas if the ventilator
supports the patient in response to his/her
request, but the assistance is not appropriate
(delayed or not suffi cient), the asynchrony is
‘minor’. Mojoli et al pointed out that minor
asynchronies might have a greater impact than
major ones in ventilated ICU patients. 11
Aetiology
Some asynchronies are typically associated with
a low patient respiratory drive and/or a too high
ventilator assistance (ineffective efforts, delayed
cycling, autotriggering, reverse triggering); others
are associated with high respiratory drive and low
ventilator support, such as early cycling and
double triggering. 12
Clinical relevance
The fi rst aspect to consider is the prevalence of
asynchronies: they are very common during
ventilation, not only in assisted modes but also in
controlled modes. In 1997, Chao et al 13 observed
200 patients during the weaning from mechanical
ventilation and found that 10% of them had
ineffective efforts; this phenomenon was
associated with prolonged and diffi cult weaning.
This was the fi rst large study focusing on
patient–ventilator asynchronies. Following on
from this, there was an increasing interest on the
subject; other studies confi rmed the high
prevalence of asynchronies in ICU patients,
clarifying their clinical impact as well.
Asynchronies started to be considered not only
as a cause of discomfort for patients, 14 but also as
a cause of prolonged mechanical ventilation, 6,15
muscle injury, higher sedation requirements, 16
and eventually increased mortality. 4 Moreover,
asynchronies could be involved in long-term
neuropsychological effects in patients with
respiratory distress. 17,18
Clinicians applied different monitoring tools
to detect asynchronies (oesophageal pressure,
diaphragm electrical activity), and manufacturers
produced new modes of ventilation aiming to
better fi t patients’ requirements (Table 1).
Clinicians progressively learnt how to visually
detect asynchronies by looking at ventilator
waveforms at the bedside and to adapt ventilator
settings breath by breath accordingly, but also
realised that the time required for such
management was not compatible with everyday
clinical practice in the ICU. In fact, patient–
ventilator interaction is highly variable among
different patients and, in the same patient at
different times. 4 Moreover, it was suggested that
brief clusters of asynchronies and more than
average frequency of asynchronies, are associated
with poor outcome. 19 But it is not feasible to stay
at the bedside 24/7 to monitor asynchronies and
change the ventilator’s setting according to
waveforms. In this context, researchers and
manufacturers put their efforts into developing
new technologies that are able to replace
clinicians in analysing ventilator waveforms and
detecting patients’ respiratory activity. 1
Detecting asynchronies at the bedside
One can approach asynchronies in two different
ways: the fi rst is to rely on software and
10
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The waveform
method is
based on the
identifi cation
of a patient’s
spontaneous
activity