J R Soc Med 2005;98:7-9
doi:10.1258/jrsm.98.1.7
© 2005 Royal Society of Medicine
Fuzzy logic and decision-making in anaesthetics
Paul Grant MB BSc
Ole Naesh MD PhD
Department of Anaesthetics, Timaru Hospital, Queen Street, Timaru, South
Canterbury Private Bag 911, New Zealand
Correspondence to: Dr Paul Grant E-mail:
drpaul.grant{at}orange.net
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INTRODUCTION
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The task of the anaesthetist is to control the continuum between
consciousness
and unconsciousness, pain and analgesia, muscle activity and
relaxation—inhibition
of activation and enhancement of
inhibition.
1 During
an operation,
an anaesthetized patient is part of a 'feedback
circuit' (
Figure 1).
Changes in variables such as blood pressure and respiratory
rate are monitored
and stability is restored by adjustments
to ventilation and drug
dosage.
2 The
decision-maker and controller
in this loop is the anaesthetist, who will make
an individual
judgment on how best to respond to, say, low blood pressure,
tachypnoea
or a decreasing oxygen saturation.
Computer programs employing 'fuzzy logic' are intended to imitate
human thought processes in these complex circumstances but to function at
greater speed. A simple computerized system might be based on the rule
'if X then do Y'. The drawback of such programs is
that a large number of rules are needed to deal with every possible situation.
In addition, if two or more indices are being measured the rule then becomes
'if X and Y, then Z' and the number of
rules multiplies vastly. Fuzzy logic works by drastically reducing the number
of rules and using proportionate amounts of each rule; and it can also
'learn' by assessing responses to changes in output. It thus opens
the way to automation in circumstances that would be difficult or impossible
to model with simple linear mathematics.
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APPLICATIONS
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'Adaptive controller' is the name given to a system with
adjustable
inputs and outputs and a mechanism for altering them. It contains
two
loops—a control loop and a parameter adjustment loop.
The potential
applications of such systems in medicine are very
wide.
1-3
The
following examples illustrate the scope of fuzzy logic in the
complex
dynamic circumstances of anaesthesia.
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Control of mechanical ventilation
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Artificial ventilation of the lungs represents a continuous
process during
which arterial pO
2 and pCO
2 must be maintained
at
optimal levels consistent with avoidance of lung damage,
cardiac failure and
respiratory muscle fatigue. In a series
of ventilated patients Schaublin and
co-workers
4 tested a
fuzzy
logic program that monitored pO
2 and end-tidal CO
2
and altered
ventilatory frequency and tidal volume to keep end-tidal
CO
2 at a desired level. The system was deemed to perform no less
well
than anaesthetists using conventional techniques under similar
conditions.
Weaning from the ventilator, in a patient with respiratory insufficiency,
is another procedure where there is no universally agreed approach. For
determining the need for pressure support ventilation in intensive care, fuzzy
logic systems have employed measurements of heart rate, tidal volume,
breathing frequency and oxygen saturation. Nemoto et
al.5 found that
the computerized system agreed 88% of the time with its human counterparts
concerning pressure adjustments and was somewhat less aggressive in reducing
support levels of ventilation. Whether the patient is safer with this system
remains to be determined.
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Control of anaesthetic gases and blood pressure
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When used to control dosage of volatile anaesthetic agents fuzzy
logic
systems have again performed almost as well as anaesthetists.
For example,
Sieber and
colleagues
6 reported
accurate control
of mean alveolar concentration of isoflurane by a system that
altered
the gas flow rates. In one study, during minimal flow anaesthesia,
fuzzy-logic
control of inspired oxygen, nitrous oxide and inspired isoflurane
was
actually
superior
7—an
observation that might lead to economies
in the use of this expensive
agent.
One of the most important measurements for estimating the required dose of
inhaled anaesthetics and judging the haemodynamic status of the patient is
arterial blood pressure. The ease of measurement and the speed at which it
reacts to change make it suitable for feedback in systems that control the
depth of anaesthesia. Zbinden et
al.8 employed a
system that regulated the inspired isoflurane concentration in response to
changes in blood pressure, alternating this with manual techniques. At skin
incision the fuzzy logic system performed better in terms of blood pressure
control, but at subsequent intra-abdominal operation it performed slightly
worse. The same system can work in reverse if a stable blood pressure during
operation is the most important output to address.
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Postoperative pain control
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Patient-controlled analgesia (PCA), a supply and demand method
managed by
the patient's conscious feedback, has been a considerable
advance in
control of postoperative pain. However, drawbacks
are that patients may be too
drowsy or confused postoperatively
to work it, the pain may be too great to
allow them to move,
the PCA button may be hard to find, and patients may opt
not
to use it because of stoicism or failure to recognize the nature
of the
discomfort they are experiencing. For these reasons,
analgesic perfusion pumps
have been developed that act under
fuzzy logic guidance. The best example is
an opioid infusion
system that reacts to the patient's pain responses,
titrating
the effect of miniboluses and halting the infusion in the event
of
desaturation, bradypnoea, or large changes in pulse rate
or blood pressure.
The patient's target analgesia level was
achieved 77% of the
time.
9 These devices
are also applicable
to work on laboratory animals, where the pain markers can
include
catecholamine concentrations and pain fibre
activity.
10
Although
not yet perfect, fuzzy logic systems do seem promising for management
of
post-operative pain, in conjunction with PCA.
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Neuromuscular blockade
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The responses of patients to muscle relaxants are highly variable,
and
neuromuscular blockade is an emerging area for the use of
fuzzy logic. The
rate of drug delivery can be adjusted in terms
of feedback from a sensor that
measures muscle relaxation. Studies
have been reported with the
non-depolarizing agents
atracurium,
11
pancuronium
12 and
rocuronium.
13 The
fuzzy logic adaptation scheme measures
the difference between predicted and
measured responses (neuromuscular
excitability with a train-of-four
stimulator), learns from it
and adapts the model to provide optimum
neuromuscular blockade.
Since the drug is infused continuously rather than
given in
boluses, the degree of relaxation varies little and patients
receive
the minimum amount necessary to achieve adequate
relaxation.
14
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DISCUSSION
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In the above examples, fuzzy logic usually matched the performance
of an
anaesthetist and sometimes exceeded it. Automated 'intelligent'
systems
of this sort are more reliable than manual interventions (they
are not
prone to stress and fatigue) and by reducing routine
workload they should
allow the anaesthetist to focus more on
critical events. There have been
occasions when fuzzy logic
systems did not match routine performance by an
anaesthetist,
but this may be a matter of inadequate programming (fuzzy logic
still
requires an expert anaesthetist to set the rules). Also fuzzy
logic
lacks clinical intuition; an advantage of human anaesthetists
is that they
sometimes rightly ignore the rules. However, failures
are rare, and the
ability to learn and adapt gives them far
greater potential than alternative
computerized methods such
as fixed Boolean
algorithms.
1,15
Moreover, their output is a
smooth function rather than a lurch between all or
nothing.
Enthusiasts for fuzzy logic in anaesthetics envisage an allencompassing
system that monitors vital signs and interdependently controls ventilation,
relaxation, haemodynamic status, analgesia and sedation. However, nobody sees
this replacing the anaesthetist: the aim is to enhance the anaesthetist's
potential.
A possible future avenue for the use of fuzzy logic is in the management of
patients where no fully trained anaesthetist is available. And, even in a
large hospital, one can imagine a single anaesthetist being able to circulate
from theatre to theatre, supervising multiple operations and dealing with
unpredicted events while fuzzy logic systems make automatic adjustments
determined by specific guidelines. The potential applications for fuzzy logic
are limited only by our imaginations.
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INTRODUCTION
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