Abstract
Background: Nociception of an anaesthetized patient is
difficult to assess, and no numeric methods exist for
assessing it during surgery. Since the adequacy of
analgesia depends on the interaction of several aspects,
a multi-variate approach may be needed. We developed a
response index of nociception (RN) to correlate with the
clinical assessment of the level of probable nociception
at the time of incision during propofol-remifentanil
anaesthesia. For the clinical assessment a new index
(Stimulus, Analgesia, and clinical Signs Score, SASS) was
defined. The SASS provides a combination score for the
estimated effect site concentration of delivered
analgetic, the stimulus intensity (e.g., size of
incision), and the presence or absence of any clinical
signs of inadequate analgesia (movements, coughing etc.).
Methods: The study was approved by the local ethical
committee and written informed consent was obtained from
each patient. Electrocardiographic (ECG),
photoplethysmographic (PPG) and electroencephalographic
(EEG) spectral entropy signals were recorded in 55
females during propofol-remifentanil anaesthesia. Depth
of anaesthesia was targeted to maintain the response
entropy (RE) value between 35 and 60, and remifentanil
plasma concentration levels of 1, 3 or 5 ng/ml were
obtained randomly before the moment of the first
incision. Two independent clinicians estimated the SASS
at the time of incision on the basis of the annotations
made by a trained nurse, who was instructed to carefully
note any clinical sign showing possibly inadequate
analgesia. The SASS was from 0 to 12 corresponding to no
nociception vs. extreme nociception, respectively.
Physiological signals were collected using a Datex-Ohmeda
S/5 Anesthesia Monitor and were stored on a PC. Features
representing signal shape descriptors and covering time-
and frequency domain information were extracted off-line
from these signals at the time of the incision. Absolute
as well as relative features were calculated. An
algorithm using combinations of signal features was
developed to map the monitored signal features to the
three different components constituting the SASS (i.e.,
level of analgesic drug, level of stimulation, and
presence of clinical signs). The outputs of these three
modules were then combined to provide the RN (i.e.,
estimation of SASS on the basis of monitored signals).
The performance of the RN was assessed by the root mean
square error (RMSE) and Pearson's correlation coefficient
for the module estimating remifentanil level, and
prediction probability (pk) for the components reflecting
clinical observations and stimulus intensity.
Results: Different features of HRV, response entropy (RE)
and PPG variability were combined for different modules:
HRV and RE for estimation of remifentanil level and
presence of clinical signs, and HRV and PPG variability
for estimation of stimulus intensity. For remifentanil
the correlation coefficient, r = 0.75 (p = 8e-11) and
RMSE 1.1 ng/ml; for clinical observation pk = 0.62
(SE=0.04); and for stimulus intensity pk=0.57 (SE=0.04)
were reached. The pk of the eventual SASS estimation is
0.77 (SE=0.03).
Discussion: A modular algorithm was developed to estimate
the nociception at the time of incision. This modular
approach provides a more robust, easy maintainable and
tunable, and intuitive implementation as compared to the
one in which the final index would be estimated directly
in one step.
Original language | English |
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Publication status | Published - 2005 |
MoE publication type | Not Eligible |
Event | Advanced Modelling and Control in Anesthesia Conference, AMCA 2005 - Monte Verita, Switzerland Duration: 10 Apr 2005 → 14 Apr 2005 |
Conference
Conference | Advanced Modelling and Control in Anesthesia Conference, AMCA 2005 |
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Country | Switzerland |
City | Monte Verita |
Period | 10/04/05 → 14/04/05 |